Pandas - I.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "38f628ad",
   "metadata": {},
   "source": [
    "# <font color=hotpink> Python Pandas - I </font>\n",
    "\n",
    "* Pandas is a Python library that provides utilities to deal with structured data stored in the form of rows and columns.\n",
    "* According to Wikipedia page on Pandas, the name is derived from the term \"*panel data*\", term used for multidimensional structured data sets.\n",
    "* Robust toolkit for analyzing, filtering, manipulating, aggregating, merging, pivoting, and cleaning data.\n",
    "* Can be called as \"*Excel for Python*\" or \"*Excel on Steroids*\", metaphorically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5cb693a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec84f335",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.5.2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7e2792a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.24.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "972f6f10",
   "metadata": {},
   "source": [
    "## <font color=fe7401> Series </font>\n",
    "\n",
    "* One-dimensional ndarray holding data of any type.\n",
    "* Series combine both list and dictionary Python data-structure.\n",
    "* It allows to store data in order like list, but also allows to assign identifier like dictionary.\n",
    "* By default Pandas assign identifier/key as index like 0, 1, 2 ... to the data.\n",
    "* Difference between Pandas series and dictionary is that series can have duplicate keys. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a4456147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Vanilla\n",
       "1     Chocolate\n",
       "2    Strawberry\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creation of series\n",
    "\n",
    "ice_cream = ['Vanilla', 'Chocolate', 'Strawberry']\n",
    "pd.Series(ice_cream)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aa887e34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    11\n",
       "1    22\n",
       "2    33\n",
       "3    44\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# series of int data type\n",
    "\n",
    "pd.Series([11, 22, 33, 44])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f54e3624",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Apple\n",
       "1       12\n",
       "2    99.56\n",
       "3     True\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# series of heterogeneous dtype\n",
    "\n",
    "pd.Series([\"Apple\", 12, 99.56, True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c5943a2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sachin    99\n",
       "Virat     56\n",
       "Brown     89\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# series from dictionary\n",
    "\n",
    "runs_score = {\n",
    "    \"Sachin\": 99,\n",
    "    \"Virat\": 56,\n",
    "    \"Brown\": 89\n",
    "}\n",
    "\n",
    "pd.Series(runs_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ba488fd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameter - the name we give to expected input\n",
    "# argument - concrete value we provide to a parameter\n",
    "\n",
    "# Difficuty (param) - Easy, Medium, Hard (argument)\n",
    "# pd.Series(data = dataSrc, index = idx)\n",
    "# param: data, index\n",
    "# arg: dataSrc, idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1339f28e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Apple            Monday\n",
       "Mango           Tuesday\n",
       "Kiwi          Wednesday\n",
       "Strawberry     Thursday\n",
       "Apple            Friday\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# duplicate keys allowed in series\n",
    "\n",
    "weekdays = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']\n",
    "fruits = ['Apple', 'Mango', 'Kiwi', 'Strawberry', 'Apple']\n",
    "\n",
    "pd.Series(data = weekdays, index = fruits)   # keyword args"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef86566",
   "metadata": {},
   "source": [
    "### <font color=blue> Methods on Series </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1aea6722",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    12.00\n",
       "1    23.56\n",
       "2     8.50\n",
       "3      NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices = pd.Series([12, 23.56, 8.5, None])\n",
    "prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "05e1cb85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count method will ignore any N/A or Null values\n",
    "\n",
    "prices.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "07d3f54e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44.06"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c661b65c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2403.12"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.product()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "32569738",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.686666666666667"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8b0ac4f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ace032a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     3.000000\n",
       "mean     14.686667\n",
       "std       7.881277\n",
       "min       8.500000\n",
       "25%      10.250000\n",
       "50%      12.000000\n",
       "75%      17.780000\n",
       "max      23.560000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a134f217",
   "metadata": {},
   "source": [
    "### <font color=blue> Attributes on Series </font>\n",
    "\n",
    "* Driving a car is action/method, whereas car's color is detail/attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "91af918f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Good\n",
       "1    Handsome\n",
       "2       Smart\n",
       "3        Tall\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjectives = pd.Series([\"Good\", \"Handsome\", \"Smart\", \"Tall\"])\n",
    "adjectives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "288da31e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjectives.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9c874ec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjectives.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f9e2484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Good', 'Handsome', 'Smart', 'Tall'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjectives.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "07d0abff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(adjectives.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4e4d9772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjectives.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ee203fef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.range.RangeIndex"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(adjectives.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ef7d4980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjectives.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "203cfc9a",
   "metadata": {},
   "source": [
    "### <font color=blue> Import Series with pd.read_csv() </font>\n",
    "\n",
    "* pd.read_csv() returns DataFrame object.\n",
    "* Dataframe visualize as alternate colors in rows, whereas Series is a plain black & white text in the output of a cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "12863b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pokemon</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Venusaur</td>\n",
       "      <td>Grass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charmander</td>\n",
       "      <td>Fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Charmeleon</td>\n",
       "      <td>Fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>Yveltal</td>\n",
       "      <td>Dark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>Zygarde</td>\n",
       "      <td>Dragon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>Diancie</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>Hoopa</td>\n",
       "      <td>Psychic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>Volcanion</td>\n",
       "      <td>Fire</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>721 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Pokemon     Type\n",
       "0     Bulbasaur    Grass\n",
       "1       Ivysaur    Grass\n",
       "2      Venusaur    Grass\n",
       "3    Charmander     Fire\n",
       "4    Charmeleon     Fire\n",
       "..          ...      ...\n",
       "716     Yveltal     Dark\n",
       "717     Zygarde   Dragon\n",
       "718     Diancie     Rock\n",
       "719       Hoopa  Psychic\n",
       "720   Volcanion     Fire\n",
       "\n",
       "[721 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"./datasets/pokemon.csv\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8879be59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Bulbasaur\n",
       "1         Ivysaur\n",
       "2        Venusaur\n",
       "3      Charmander\n",
       "4      Charmeleon\n",
       "          ...    \n",
       "716       Yveltal\n",
       "717       Zygarde\n",
       "718       Diancie\n",
       "719         Hoopa\n",
       "720     Volcanion\n",
       "Name: Pokemon, Length: 721, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get the series ie. 1-d array\n",
    "\n",
    "pokemon = pd.read_csv(\"./datasets/pokemon.csv\", usecols=[\"Pokemon\"]).squeeze(\"columns\")\n",
    "pokemon"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaf99c7a",
   "metadata": {},
   "source": [
    "### <font color=blue> head and tail method on series </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c3524f01",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Bulbasaur\n",
       "1       Ivysaur\n",
       "2      Venusaur\n",
       "3    Charmander\n",
       "4    Charmeleon\n",
       "Name: Pokemon, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "07ad9647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Bulbasaur\n",
       "1      Ivysaur\n",
       "Name: Pokemon, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "762340f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "716      Yveltal\n",
       "717      Zygarde\n",
       "718      Diancie\n",
       "719        Hoopa\n",
       "720    Volcanion\n",
       "Name: Pokemon, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de7d8fb5",
   "metadata": {},
   "source": [
    "### <font color=blue> Views versus Copies </font>\n",
    "\n",
    "* Some operations in pandas (and numpy as well) will return views of the original data, while other copies. \n",
    "* To put it very simply, a view is a subset of the original object ( DataFrame or Series ) linked to the original source, while a copy is an entirely new object ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a861f0ab",
   "metadata": {},
   "source": [
    "### <font color=blue>Passing Series to Python built-in functions </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1cd5cad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "721"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pokemon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8d7cc0bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Abomasnow',\n",
       " 'Abra',\n",
       " 'Absol',\n",
       " 'Accelgor',\n",
       " 'Aegislash',\n",
       " 'Aerodactyl',\n",
       " 'Aggron',\n",
       " 'Aipom',\n",
       " 'Alakazam',\n",
       " 'Alomomola',\n",
       " 'Altaria',\n",
       " 'Amaura',\n",
       " 'Ambipom',\n",
       " 'Amoonguss',\n",
       " 'Ampharos',\n",
       " 'Anorith',\n",
       " 'Arbok',\n",
       " 'Arcanine',\n",
       " 'Arceus',\n",
       " 'Archen',\n",
       " 'Archeops',\n",
       " 'Ariados',\n",
       " 'Armaldo',\n",
       " 'Aromatisse',\n",
       " 'Aron',\n",
       " 'Articuno',\n",
       " 'Audino',\n",
       " 'Aurorus',\n",
       " 'Avalugg',\n",
       " 'Axew',\n",
       " 'Azelf',\n",
       " 'Azumarill',\n",
       " 'Azurill',\n",
       " 'Bagon',\n",
       " 'Baltoy',\n",
       " 'Banette',\n",
       " 'Barbaracle',\n",
       " 'Barboach',\n",
       " 'Basculin',\n",
       " 'Bastiodon',\n",
       " 'Bayleef',\n",
       " 'Beartic',\n",
       " 'Beautifly',\n",
       " 'Beedrill',\n",
       " 'Beheeyem',\n",
       " 'Beldum',\n",
       " 'Bellossom',\n",
       " 'Bellsprout',\n",
       " 'Bergmite',\n",
       " 'Bibarel',\n",
       " 'Bidoof',\n",
       " 'Binacle',\n",
       " 'Bisharp',\n",
       " 'Blastoise',\n",
       " 'Blaziken',\n",
       " 'Blissey',\n",
       " 'Blitzle',\n",
       " 'Boldore',\n",
       " 'Bonsly',\n",
       " 'Bouffalant',\n",
       " 'Braixen',\n",
       " 'Braviary',\n",
       " 'Breloom',\n",
       " 'Bronzong',\n",
       " 'Bronzor',\n",
       " 'Budew',\n",
       " 'Buizel',\n",
       " 'Bulbasaur',\n",
       " 'Buneary',\n",
       " 'Bunnelby',\n",
       " 'Burmy',\n",
       " 'Butterfree',\n",
       " 'Cacnea',\n",
       " 'Cacturne',\n",
       " 'Camerupt',\n",
       " 'Carbink',\n",
       " 'Carnivine',\n",
       " 'Carracosta',\n",
       " 'Carvanha',\n",
       " 'Cascoon',\n",
       " 'Castform',\n",
       " 'Caterpie',\n",
       " 'Celebi',\n",
       " 'Chandelure',\n",
       " 'Chansey',\n",
       " 'Charizard',\n",
       " 'Charmander',\n",
       " 'Charmeleon',\n",
       " 'Chatot',\n",
       " 'Cherrim',\n",
       " 'Cherubi',\n",
       " 'Chesnaught',\n",
       " 'Chespin',\n",
       " 'Chikorita',\n",
       " 'Chimchar',\n",
       " 'Chimecho',\n",
       " 'Chinchou',\n",
       " 'Chingling',\n",
       " 'Cinccino',\n",
       " 'Clamperl',\n",
       " 'Clauncher',\n",
       " 'Clawitzer',\n",
       " 'Claydol',\n",
       " 'Clefable',\n",
       " 'Clefairy',\n",
       " 'Cleffa',\n",
       " 'Cloyster',\n",
       " 'Cobalion',\n",
       " 'Cofagrigus',\n",
       " 'Combee',\n",
       " 'Combusken',\n",
       " 'Conkeldurr',\n",
       " 'Corphish',\n",
       " 'Corsola',\n",
       " 'Cottonee',\n",
       " 'Cradily',\n",
       " 'Cranidos',\n",
       " 'Crawdaunt',\n",
       " 'Cresselia',\n",
       " 'Croagunk',\n",
       " 'Crobat',\n",
       " 'Croconaw',\n",
       " 'Crustle',\n",
       " 'Cryogonal',\n",
       " 'Cubchoo',\n",
       " 'Cubone',\n",
       " 'Cyndaquil',\n",
       " 'Darkrai',\n",
       " 'Darmanitan',\n",
       " 'Darumaka',\n",
       " 'Dedenne',\n",
       " 'Deerling',\n",
       " 'Deino',\n",
       " 'Delcatty',\n",
       " 'Delibird',\n",
       " 'Delphox',\n",
       " 'Deoxys',\n",
       " 'Dewgong',\n",
       " 'Dewott',\n",
       " 'Dialga',\n",
       " 'Diancie',\n",
       " 'Diggersby',\n",
       " 'Diglett',\n",
       " 'Ditto',\n",
       " 'Dodrio',\n",
       " 'Doduo',\n",
       " 'Donphan',\n",
       " 'Doublade',\n",
       " 'Dragalge',\n",
       " 'Dragonair',\n",
       " 'Dragonite',\n",
       " 'Drapion',\n",
       " 'Dratini',\n",
       " 'Drifblim',\n",
       " 'Drifloon',\n",
       " 'Drilbur',\n",
       " 'Drowzee',\n",
       " 'Druddigon',\n",
       " 'Ducklett',\n",
       " 'Dugtrio',\n",
       " 'Dunsparce',\n",
       " 'Duosion',\n",
       " 'Durant',\n",
       " 'Dusclops',\n",
       " 'Dusknoir',\n",
       " 'Duskull',\n",
       " 'Dustox',\n",
       " 'Dwebble',\n",
       " 'Eelektrik',\n",
       " 'Eelektross',\n",
       " 'Eevee',\n",
       " 'Ekans',\n",
       " 'Electabuzz',\n",
       " 'Electivire',\n",
       " 'Electrike',\n",
       " 'Electrode',\n",
       " 'Elekid',\n",
       " 'Elgyem',\n",
       " 'Emboar',\n",
       " 'Emolga',\n",
       " 'Empoleon',\n",
       " 'Entei',\n",
       " 'Escavalier',\n",
       " 'Espeon',\n",
       " 'Espurr',\n",
       " 'Excadrill',\n",
       " 'Exeggcute',\n",
       " 'Exeggutor',\n",
       " 'Exploud',\n",
       " \"Farfetch'd\",\n",
       " 'Fearow',\n",
       " 'Feebas',\n",
       " 'Fennekin',\n",
       " 'Feraligatr',\n",
       " 'Ferroseed',\n",
       " 'Ferrothorn',\n",
       " 'Finneon',\n",
       " 'Flaaffy',\n",
       " 'Flabébé',\n",
       " 'Flareon',\n",
       " 'Fletchinder',\n",
       " 'Fletchling',\n",
       " 'Floatzel',\n",
       " 'Floette',\n",
       " 'Florges',\n",
       " 'Flygon',\n",
       " 'Foongus',\n",
       " 'Forretress',\n",
       " 'Fraxure',\n",
       " 'Frillish',\n",
       " 'Froakie',\n",
       " 'Frogadier',\n",
       " 'Froslass',\n",
       " 'Furfrou',\n",
       " 'Furret',\n",
       " 'Gabite',\n",
       " 'Gallade',\n",
       " 'Galvantula',\n",
       " 'Garbodor',\n",
       " 'Garchomp',\n",
       " 'Gardevoir',\n",
       " 'Gastly',\n",
       " 'Gastrodon',\n",
       " 'Genesect',\n",
       " 'Gengar',\n",
       " 'Geodude',\n",
       " 'Gible',\n",
       " 'Gigalith',\n",
       " 'Girafarig',\n",
       " 'Giratina',\n",
       " 'Glaceon',\n",
       " 'Glalie',\n",
       " 'Glameow',\n",
       " 'Gligar',\n",
       " 'Gliscor',\n",
       " 'Gloom',\n",
       " 'Gogoat',\n",
       " 'Golbat',\n",
       " 'Goldeen',\n",
       " 'Golduck',\n",
       " 'Golem',\n",
       " 'Golett',\n",
       " 'Golurk',\n",
       " 'Goodra',\n",
       " 'Goomy',\n",
       " 'Gorebyss',\n",
       " 'Gothita',\n",
       " 'Gothitelle',\n",
       " 'Gothorita',\n",
       " 'Gourgeist',\n",
       " 'Granbull',\n",
       " 'Graveler',\n",
       " 'Greninja',\n",
       " 'Grimer',\n",
       " 'Grotle',\n",
       " 'Groudon',\n",
       " 'Grovyle',\n",
       " 'Growlithe',\n",
       " 'Grumpig',\n",
       " 'Gulpin',\n",
       " 'Gurdurr',\n",
       " 'Gyarados',\n",
       " 'Happiny',\n",
       " 'Hariyama',\n",
       " 'Haunter',\n",
       " 'Hawlucha',\n",
       " 'Haxorus',\n",
       " 'Heatmor',\n",
       " 'Heatran',\n",
       " 'Heliolisk',\n",
       " 'Helioptile',\n",
       " 'Heracross',\n",
       " 'Herdier',\n",
       " 'Hippopotas',\n",
       " 'Hippowdon',\n",
       " 'Hitmonchan',\n",
       " 'Hitmonlee',\n",
       " 'Hitmontop',\n",
       " 'Ho-oh',\n",
       " 'Honchkrow',\n",
       " 'Honedge',\n",
       " 'Hoopa',\n",
       " 'Hoothoot',\n",
       " 'Hoppip',\n",
       " 'Horsea',\n",
       " 'Houndoom',\n",
       " 'Houndour',\n",
       " 'Huntail',\n",
       " 'Hydreigon',\n",
       " 'Hypno',\n",
       " 'Igglybuff',\n",
       " 'Illumise',\n",
       " 'Infernape',\n",
       " 'Inkay',\n",
       " 'Ivysaur',\n",
       " 'Jellicent',\n",
       " 'Jigglypuff',\n",
       " 'Jirachi',\n",
       " 'Jolteon',\n",
       " 'Joltik',\n",
       " 'Jumpluff',\n",
       " 'Jynx',\n",
       " 'Kabuto',\n",
       " 'Kabutops',\n",
       " 'Kadabra',\n",
       " 'Kakuna',\n",
       " 'Kangaskhan',\n",
       " 'Karrablast',\n",
       " 'Kecleon',\n",
       " 'Keldeo',\n",
       " 'Kingdra',\n",
       " 'Kingler',\n",
       " 'Kirlia',\n",
       " 'Klang',\n",
       " 'Klefki',\n",
       " 'Klink',\n",
       " 'Klinklang',\n",
       " 'Koffing',\n",
       " 'Krabby',\n",
       " 'Kricketot',\n",
       " 'Kricketune',\n",
       " 'Krokorok',\n",
       " 'Krookodile',\n",
       " 'Kyogre',\n",
       " 'Kyurem',\n",
       " 'Lairon',\n",
       " 'Lampent',\n",
       " 'Landorus',\n",
       " 'Lanturn',\n",
       " 'Lapras',\n",
       " 'Larvesta',\n",
       " 'Larvitar',\n",
       " 'Latias',\n",
       " 'Latios',\n",
       " 'Leafeon',\n",
       " 'Leavanny',\n",
       " 'Ledian',\n",
       " 'Ledyba',\n",
       " 'Lickilicky',\n",
       " 'Lickitung',\n",
       " 'Liepard',\n",
       " 'Lileep',\n",
       " 'Lilligant',\n",
       " 'Lillipup',\n",
       " 'Linoone',\n",
       " 'Litleo',\n",
       " 'Litwick',\n",
       " 'Lombre',\n",
       " 'Lopunny',\n",
       " 'Lotad',\n",
       " 'Loudred',\n",
       " 'Lucario',\n",
       " 'Ludicolo',\n",
       " 'Lugia',\n",
       " 'Lumineon',\n",
       " 'Lunatone',\n",
       " 'Luvdisc',\n",
       " 'Luxio',\n",
       " 'Luxray',\n",
       " 'Machamp',\n",
       " 'Machoke',\n",
       " 'Machop',\n",
       " 'Magby',\n",
       " 'Magcargo',\n",
       " 'Magikarp',\n",
       " 'Magmar',\n",
       " 'Magmortar',\n",
       " 'Magnemite',\n",
       " 'Magneton',\n",
       " 'Magnezone',\n",
       " 'Makuhita',\n",
       " 'Malamar',\n",
       " 'Mamoswine',\n",
       " 'Manaphy',\n",
       " 'Mandibuzz',\n",
       " 'Manectric',\n",
       " 'Mankey',\n",
       " 'Mantine',\n",
       " 'Mantyke',\n",
       " 'Maractus',\n",
       " 'Mareep',\n",
       " 'Marill',\n",
       " 'Marowak',\n",
       " 'Marshtomp',\n",
       " 'Masquerain',\n",
       " 'Mawile',\n",
       " 'Medicham',\n",
       " 'Meditite',\n",
       " 'Meganium',\n",
       " 'Meloetta',\n",
       " 'Meowstic',\n",
       " 'Meowth',\n",
       " 'Mesprit',\n",
       " 'Metagross',\n",
       " 'Metang',\n",
       " 'Metapod',\n",
       " 'Mew',\n",
       " 'Mewtwo',\n",
       " 'Mienfoo',\n",
       " 'Mienshao',\n",
       " 'Mightyena',\n",
       " 'Milotic',\n",
       " 'Miltank',\n",
       " 'Mime Jr.',\n",
       " 'Minccino',\n",
       " 'Minun',\n",
       " 'Misdreavus',\n",
       " 'Mismagius',\n",
       " 'Moltres',\n",
       " 'Monferno',\n",
       " 'Mothim',\n",
       " 'Mr. Mime',\n",
       " 'Mudkip',\n",
       " 'Muk',\n",
       " 'Munchlax',\n",
       " 'Munna',\n",
       " 'Murkrow',\n",
       " 'Musharna',\n",
       " 'Natu',\n",
       " 'Nidoking',\n",
       " 'Nidoqueen',\n",
       " 'Nidoran',\n",
       " 'Nidoran♂',\n",
       " 'Nidorina',\n",
       " 'Nidorino',\n",
       " 'Nincada',\n",
       " 'Ninetales',\n",
       " 'Ninjask',\n",
       " 'Noctowl',\n",
       " 'Noibat',\n",
       " 'Noivern',\n",
       " 'Nosepass',\n",
       " 'Numel',\n",
       " 'Nuzleaf',\n",
       " 'Octillery',\n",
       " 'Oddish',\n",
       " 'Omanyte',\n",
       " 'Omastar',\n",
       " 'Onix',\n",
       " 'Oshawott',\n",
       " 'Pachirisu',\n",
       " 'Palkia',\n",
       " 'Palpitoad',\n",
       " 'Pancham',\n",
       " 'Pangoro',\n",
       " 'Panpour',\n",
       " 'Pansage',\n",
       " 'Pansear',\n",
       " 'Paras',\n",
       " 'Parasect',\n",
       " 'Patrat',\n",
       " 'Pawniard',\n",
       " 'Pelipper',\n",
       " 'Persian',\n",
       " 'Petilil',\n",
       " 'Phanpy',\n",
       " 'Phantump',\n",
       " 'Phione',\n",
       " 'Pichu',\n",
       " 'Pidgeot',\n",
       " 'Pidgeotto',\n",
       " 'Pidgey',\n",
       " 'Pidove',\n",
       " 'Pignite',\n",
       " 'Pikachu',\n",
       " 'Piloswine',\n",
       " 'Pineco',\n",
       " 'Pinsir',\n",
       " 'Piplup',\n",
       " 'Plusle',\n",
       " 'Politoed',\n",
       " 'Poliwag',\n",
       " 'Poliwhirl',\n",
       " 'Poliwrath',\n",
       " 'Ponyta',\n",
       " 'Poochyena',\n",
       " 'Porygon',\n",
       " 'Porygon-Z',\n",
       " 'Porygon2',\n",
       " 'Primeape',\n",
       " 'Prinplup',\n",
       " 'Probopass',\n",
       " 'Psyduck',\n",
       " 'Pumpkaboo',\n",
       " 'Pupitar',\n",
       " 'Purrloin',\n",
       " 'Purugly',\n",
       " 'Pyroar',\n",
       " 'Quagsire',\n",
       " 'Quilava',\n",
       " 'Quilladin',\n",
       " 'Qwilfish',\n",
       " 'Raichu',\n",
       " 'Raikou',\n",
       " 'Ralts',\n",
       " 'Rampardos',\n",
       " 'Rapidash',\n",
       " 'Raticate',\n",
       " 'Rattata',\n",
       " 'Rayquaza',\n",
       " 'Regice',\n",
       " 'Regigigas',\n",
       " 'Regirock',\n",
       " 'Registeel',\n",
       " 'Relicanth',\n",
       " 'Remoraid',\n",
       " 'Reshiram',\n",
       " 'Reuniclus',\n",
       " 'Rhydon',\n",
       " 'Rhyhorn',\n",
       " 'Rhyperior',\n",
       " 'Riolu',\n",
       " 'Roggenrola',\n",
       " 'Roselia',\n",
       " 'Roserade',\n",
       " 'Rotom',\n",
       " 'Rufflet',\n",
       " 'Sableye',\n",
       " 'Salamence',\n",
       " 'Samurott',\n",
       " 'Sandile',\n",
       " 'Sandshrew',\n",
       " 'Sandslash',\n",
       " 'Sawk',\n",
       " 'Sawsbuck',\n",
       " 'Scatterbug',\n",
       " 'Sceptile',\n",
       " 'Scizor',\n",
       " 'Scolipede',\n",
       " 'Scrafty',\n",
       " 'Scraggy',\n",
       " 'Scyther',\n",
       " 'Seadra',\n",
       " 'Seaking',\n",
       " 'Sealeo',\n",
       " 'Seedot',\n",
       " 'Seel',\n",
       " 'Seismitoad',\n",
       " 'Sentret',\n",
       " 'Serperior',\n",
       " 'Servine',\n",
       " 'Seviper',\n",
       " 'Sewaddle',\n",
       " 'Sharpedo',\n",
       " 'Shaymin',\n",
       " 'Shedinja',\n",
       " 'Shelgon',\n",
       " 'Shellder',\n",
       " 'Shellos',\n",
       " 'Shelmet',\n",
       " 'Shieldon',\n",
       " 'Shiftry',\n",
       " 'Shinx',\n",
       " 'Shroomish',\n",
       " 'Shuckle',\n",
       " 'Shuppet',\n",
       " 'Sigilyph',\n",
       " 'Silcoon',\n",
       " 'Simipour',\n",
       " 'Simisage',\n",
       " 'Simisear',\n",
       " 'Skarmory',\n",
       " 'Skiddo',\n",
       " 'Skiploom',\n",
       " 'Skitty',\n",
       " 'Skorupi',\n",
       " 'Skrelp',\n",
       " 'Skuntank',\n",
       " 'Slaking',\n",
       " 'Slakoth',\n",
       " 'Sliggoo',\n",
       " 'Slowbro',\n",
       " 'Slowking',\n",
       " 'Slowpoke',\n",
       " 'Slugma',\n",
       " 'Slurpuff',\n",
       " 'Smeargle',\n",
       " 'Smoochum',\n",
       " 'Sneasel',\n",
       " 'Snivy',\n",
       " 'Snorlax',\n",
       " 'Snorunt',\n",
       " 'Snover',\n",
       " 'Snubbull',\n",
       " 'Solosis',\n",
       " 'Solrock',\n",
       " 'Spearow',\n",
       " 'Spewpa',\n",
       " 'Spheal',\n",
       " 'Spinarak',\n",
       " 'Spinda',\n",
       " 'Spiritomb',\n",
       " 'Spoink',\n",
       " 'Spritzee',\n",
       " 'Squirtle',\n",
       " 'Stantler',\n",
       " 'Staraptor',\n",
       " 'Staravia',\n",
       " 'Starly',\n",
       " 'Starmie',\n",
       " 'Staryu',\n",
       " 'Steelix',\n",
       " 'Stoutland',\n",
       " 'Stunfisk',\n",
       " 'Stunky',\n",
       " 'Sudowoodo',\n",
       " 'Suicune',\n",
       " 'Sunflora',\n",
       " 'Sunkern',\n",
       " 'Surskit',\n",
       " 'Swablu',\n",
       " 'Swadloon',\n",
       " 'Swalot',\n",
       " 'Swampert',\n",
       " 'Swanna',\n",
       " 'Swellow',\n",
       " 'Swinub',\n",
       " 'Swirlix',\n",
       " 'Swoobat',\n",
       " 'Sylveon',\n",
       " 'Taillow',\n",
       " 'Talonflame',\n",
       " 'Tangela',\n",
       " 'Tangrowth',\n",
       " 'Tauros',\n",
       " 'Teddiursa',\n",
       " 'Tentacool',\n",
       " 'Tentacruel',\n",
       " 'Tepig',\n",
       " 'Terrakion',\n",
       " 'Throh',\n",
       " 'Thundurus',\n",
       " 'Timburr',\n",
       " 'Tirtouga',\n",
       " 'Togekiss',\n",
       " 'Togepi',\n",
       " 'Togetic',\n",
       " 'Torchic',\n",
       " 'Torkoal',\n",
       " 'Tornadus',\n",
       " 'Torterra',\n",
       " 'Totodile',\n",
       " 'Toxicroak',\n",
       " 'Tranquill',\n",
       " 'Trapinch',\n",
       " 'Treecko',\n",
       " 'Trevenant',\n",
       " 'Tropius',\n",
       " 'Trubbish',\n",
       " 'Turtwig',\n",
       " 'Tympole',\n",
       " 'Tynamo',\n",
       " 'Typhlosion',\n",
       " 'Tyranitar',\n",
       " 'Tyrantrum',\n",
       " 'Tyrogue',\n",
       " 'Tyrunt',\n",
       " 'Umbreon',\n",
       " 'Unfezant',\n",
       " 'Unown',\n",
       " 'Ursaring',\n",
       " 'Uxie',\n",
       " 'Vanillish',\n",
       " 'Vanillite',\n",
       " 'Vanilluxe',\n",
       " 'Vaporeon',\n",
       " 'Venipede',\n",
       " 'Venomoth',\n",
       " 'Venonat',\n",
       " 'Venusaur',\n",
       " 'Vespiquen',\n",
       " 'Vibrava',\n",
       " 'Victini',\n",
       " 'Victreebel',\n",
       " 'Vigoroth',\n",
       " 'Vileplume',\n",
       " 'Virizion',\n",
       " 'Vivillon',\n",
       " 'Volbeat',\n",
       " 'Volcanion',\n",
       " 'Volcarona',\n",
       " 'Voltorb',\n",
       " 'Vullaby',\n",
       " 'Vulpix',\n",
       " 'Wailmer',\n",
       " 'Wailord',\n",
       " 'Walrein',\n",
       " 'Wartortle',\n",
       " 'Watchog',\n",
       " 'Weavile',\n",
       " 'Weedle',\n",
       " 'Weepinbell',\n",
       " 'Weezing',\n",
       " 'Whimsicott',\n",
       " 'Whirlipede',\n",
       " 'Whiscash',\n",
       " 'Whismur',\n",
       " 'Wigglytuff',\n",
       " 'Wingull',\n",
       " 'Wobbuffet',\n",
       " 'Woobat',\n",
       " 'Wooper',\n",
       " 'Wormadam',\n",
       " 'Wurmple',\n",
       " 'Wynaut',\n",
       " 'Xatu',\n",
       " 'Xerneas',\n",
       " 'Yamask',\n",
       " 'Yanma',\n",
       " 'Yanmega',\n",
       " 'Yveltal',\n",
       " 'Zangoose',\n",
       " 'Zapdos',\n",
       " 'Zebstrika',\n",
       " 'Zekrom',\n",
       " 'Zigzagoon',\n",
       " 'Zoroark',\n",
       " 'Zorua',\n",
       " 'Zubat',\n",
       " 'Zweilous',\n",
       " 'Zygarde']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(pokemon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "da63d0dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Zygarde'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(pokemon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6162d000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'Bulbasaur',\n",
       " 1: 'Ivysaur',\n",
       " 2: 'Venusaur',\n",
       " 3: 'Charmander',\n",
       " 4: 'Charmeleon',\n",
       " 5: 'Charizard',\n",
       " 6: 'Squirtle',\n",
       " 7: 'Wartortle',\n",
       " 8: 'Blastoise',\n",
       " 9: 'Caterpie',\n",
       " 10: 'Metapod',\n",
       " 11: 'Butterfree',\n",
       " 12: 'Weedle',\n",
       " 13: 'Kakuna',\n",
       " 14: 'Beedrill',\n",
       " 15: 'Pidgey',\n",
       " 16: 'Pidgeotto',\n",
       " 17: 'Pidgeot',\n",
       " 18: 'Rattata',\n",
       " 19: 'Raticate',\n",
       " 20: 'Spearow',\n",
       " 21: 'Fearow',\n",
       " 22: 'Ekans',\n",
       " 23: 'Arbok',\n",
       " 24: 'Pikachu',\n",
       " 25: 'Raichu',\n",
       " 26: 'Sandshrew',\n",
       " 27: 'Sandslash',\n",
       " 28: 'Nidoran',\n",
       " 29: 'Nidorina',\n",
       " 30: 'Nidoqueen',\n",
       " 31: 'Nidoran♂',\n",
       " 32: 'Nidorino',\n",
       " 33: 'Nidoking',\n",
       " 34: 'Clefairy',\n",
       " 35: 'Clefable',\n",
       " 36: 'Vulpix',\n",
       " 37: 'Ninetales',\n",
       " 38: 'Jigglypuff',\n",
       " 39: 'Wigglytuff',\n",
       " 40: 'Zubat',\n",
       " 41: 'Golbat',\n",
       " 42: 'Oddish',\n",
       " 43: 'Gloom',\n",
       " 44: 'Vileplume',\n",
       " 45: 'Paras',\n",
       " 46: 'Parasect',\n",
       " 47: 'Venonat',\n",
       " 48: 'Venomoth',\n",
       " 49: 'Diglett',\n",
       " 50: 'Dugtrio',\n",
       " 51: 'Meowth',\n",
       " 52: 'Persian',\n",
       " 53: 'Psyduck',\n",
       " 54: 'Golduck',\n",
       " 55: 'Mankey',\n",
       " 56: 'Primeape',\n",
       " 57: 'Growlithe',\n",
       " 58: 'Arcanine',\n",
       " 59: 'Poliwag',\n",
       " 60: 'Poliwhirl',\n",
       " 61: 'Poliwrath',\n",
       " 62: 'Abra',\n",
       " 63: 'Kadabra',\n",
       " 64: 'Alakazam',\n",
       " 65: 'Machop',\n",
       " 66: 'Machoke',\n",
       " 67: 'Machamp',\n",
       " 68: 'Bellsprout',\n",
       " 69: 'Weepinbell',\n",
       " 70: 'Victreebel',\n",
       " 71: 'Tentacool',\n",
       " 72: 'Tentacruel',\n",
       " 73: 'Geodude',\n",
       " 74: 'Graveler',\n",
       " 75: 'Golem',\n",
       " 76: 'Ponyta',\n",
       " 77: 'Rapidash',\n",
       " 78: 'Slowpoke',\n",
       " 79: 'Slowbro',\n",
       " 80: 'Magnemite',\n",
       " 81: 'Magneton',\n",
       " 82: \"Farfetch'd\",\n",
       " 83: 'Doduo',\n",
       " 84: 'Dodrio',\n",
       " 85: 'Seel',\n",
       " 86: 'Dewgong',\n",
       " 87: 'Grimer',\n",
       " 88: 'Muk',\n",
       " 89: 'Shellder',\n",
       " 90: 'Cloyster',\n",
       " 91: 'Gastly',\n",
       " 92: 'Haunter',\n",
       " 93: 'Gengar',\n",
       " 94: 'Onix',\n",
       " 95: 'Drowzee',\n",
       " 96: 'Hypno',\n",
       " 97: 'Krabby',\n",
       " 98: 'Kingler',\n",
       " 99: 'Voltorb',\n",
       " 100: 'Electrode',\n",
       " 101: 'Exeggcute',\n",
       " 102: 'Exeggutor',\n",
       " 103: 'Cubone',\n",
       " 104: 'Marowak',\n",
       " 105: 'Hitmonlee',\n",
       " 106: 'Hitmonchan',\n",
       " 107: 'Lickitung',\n",
       " 108: 'Koffing',\n",
       " 109: 'Weezing',\n",
       " 110: 'Rhyhorn',\n",
       " 111: 'Rhydon',\n",
       " 112: 'Chansey',\n",
       " 113: 'Tangela',\n",
       " 114: 'Kangaskhan',\n",
       " 115: 'Horsea',\n",
       " 116: 'Seadra',\n",
       " 117: 'Goldeen',\n",
       " 118: 'Seaking',\n",
       " 119: 'Staryu',\n",
       " 120: 'Starmie',\n",
       " 121: 'Mr. Mime',\n",
       " 122: 'Scyther',\n",
       " 123: 'Jynx',\n",
       " 124: 'Electabuzz',\n",
       " 125: 'Magmar',\n",
       " 126: 'Pinsir',\n",
       " 127: 'Tauros',\n",
       " 128: 'Magikarp',\n",
       " 129: 'Gyarados',\n",
       " 130: 'Lapras',\n",
       " 131: 'Ditto',\n",
       " 132: 'Eevee',\n",
       " 133: 'Vaporeon',\n",
       " 134: 'Jolteon',\n",
       " 135: 'Flareon',\n",
       " 136: 'Porygon',\n",
       " 137: 'Omanyte',\n",
       " 138: 'Omastar',\n",
       " 139: 'Kabuto',\n",
       " 140: 'Kabutops',\n",
       " 141: 'Aerodactyl',\n",
       " 142: 'Snorlax',\n",
       " 143: 'Articuno',\n",
       " 144: 'Zapdos',\n",
       " 145: 'Moltres',\n",
       " 146: 'Dratini',\n",
       " 147: 'Dragonair',\n",
       " 148: 'Dragonite',\n",
       " 149: 'Mewtwo',\n",
       " 150: 'Mew',\n",
       " 151: 'Chikorita',\n",
       " 152: 'Bayleef',\n",
       " 153: 'Meganium',\n",
       " 154: 'Cyndaquil',\n",
       " 155: 'Quilava',\n",
       " 156: 'Typhlosion',\n",
       " 157: 'Totodile',\n",
       " 158: 'Croconaw',\n",
       " 159: 'Feraligatr',\n",
       " 160: 'Sentret',\n",
       " 161: 'Furret',\n",
       " 162: 'Hoothoot',\n",
       " 163: 'Noctowl',\n",
       " 164: 'Ledyba',\n",
       " 165: 'Ledian',\n",
       " 166: 'Spinarak',\n",
       " 167: 'Ariados',\n",
       " 168: 'Crobat',\n",
       " 169: 'Chinchou',\n",
       " 170: 'Lanturn',\n",
       " 171: 'Pichu',\n",
       " 172: 'Cleffa',\n",
       " 173: 'Igglybuff',\n",
       " 174: 'Togepi',\n",
       " 175: 'Togetic',\n",
       " 176: 'Natu',\n",
       " 177: 'Xatu',\n",
       " 178: 'Mareep',\n",
       " 179: 'Flaaffy',\n",
       " 180: 'Ampharos',\n",
       " 181: 'Bellossom',\n",
       " 182: 'Marill',\n",
       " 183: 'Azumarill',\n",
       " 184: 'Sudowoodo',\n",
       " 185: 'Politoed',\n",
       " 186: 'Hoppip',\n",
       " 187: 'Skiploom',\n",
       " 188: 'Jumpluff',\n",
       " 189: 'Aipom',\n",
       " 190: 'Sunkern',\n",
       " 191: 'Sunflora',\n",
       " 192: 'Yanma',\n",
       " 193: 'Wooper',\n",
       " 194: 'Quagsire',\n",
       " 195: 'Espeon',\n",
       " 196: 'Umbreon',\n",
       " 197: 'Murkrow',\n",
       " 198: 'Slowking',\n",
       " 199: 'Misdreavus',\n",
       " 200: 'Unown',\n",
       " 201: 'Wobbuffet',\n",
       " 202: 'Girafarig',\n",
       " 203: 'Pineco',\n",
       " 204: 'Forretress',\n",
       " 205: 'Dunsparce',\n",
       " 206: 'Gligar',\n",
       " 207: 'Steelix',\n",
       " 208: 'Snubbull',\n",
       " 209: 'Granbull',\n",
       " 210: 'Qwilfish',\n",
       " 211: 'Scizor',\n",
       " 212: 'Shuckle',\n",
       " 213: 'Heracross',\n",
       " 214: 'Sneasel',\n",
       " 215: 'Teddiursa',\n",
       " 216: 'Ursaring',\n",
       " 217: 'Slugma',\n",
       " 218: 'Magcargo',\n",
       " 219: 'Swinub',\n",
       " 220: 'Piloswine',\n",
       " 221: 'Corsola',\n",
       " 222: 'Remoraid',\n",
       " 223: 'Octillery',\n",
       " 224: 'Delibird',\n",
       " 225: 'Mantine',\n",
       " 226: 'Skarmory',\n",
       " 227: 'Houndour',\n",
       " 228: 'Houndoom',\n",
       " 229: 'Kingdra',\n",
       " 230: 'Phanpy',\n",
       " 231: 'Donphan',\n",
       " 232: 'Porygon2',\n",
       " 233: 'Stantler',\n",
       " 234: 'Smeargle',\n",
       " 235: 'Tyrogue',\n",
       " 236: 'Hitmontop',\n",
       " 237: 'Smoochum',\n",
       " 238: 'Elekid',\n",
       " 239: 'Magby',\n",
       " 240: 'Miltank',\n",
       " 241: 'Blissey',\n",
       " 242: 'Raikou',\n",
       " 243: 'Entei',\n",
       " 244: 'Suicune',\n",
       " 245: 'Larvitar',\n",
       " 246: 'Pupitar',\n",
       " 247: 'Tyranitar',\n",
       " 248: 'Lugia',\n",
       " 249: 'Ho-oh',\n",
       " 250: 'Celebi',\n",
       " 251: 'Treecko',\n",
       " 252: 'Grovyle',\n",
       " 253: 'Sceptile',\n",
       " 254: 'Torchic',\n",
       " 255: 'Combusken',\n",
       " 256: 'Blaziken',\n",
       " 257: 'Mudkip',\n",
       " 258: 'Marshtomp',\n",
       " 259: 'Swampert',\n",
       " 260: 'Poochyena',\n",
       " 261: 'Mightyena',\n",
       " 262: 'Zigzagoon',\n",
       " 263: 'Linoone',\n",
       " 264: 'Wurmple',\n",
       " 265: 'Silcoon',\n",
       " 266: 'Beautifly',\n",
       " 267: 'Cascoon',\n",
       " 268: 'Dustox',\n",
       " 269: 'Lotad',\n",
       " 270: 'Lombre',\n",
       " 271: 'Ludicolo',\n",
       " 272: 'Seedot',\n",
       " 273: 'Nuzleaf',\n",
       " 274: 'Shiftry',\n",
       " 275: 'Taillow',\n",
       " 276: 'Swellow',\n",
       " 277: 'Wingull',\n",
       " 278: 'Pelipper',\n",
       " 279: 'Ralts',\n",
       " 280: 'Kirlia',\n",
       " 281: 'Gardevoir',\n",
       " 282: 'Surskit',\n",
       " 283: 'Masquerain',\n",
       " 284: 'Shroomish',\n",
       " 285: 'Breloom',\n",
       " 286: 'Slakoth',\n",
       " 287: 'Vigoroth',\n",
       " 288: 'Slaking',\n",
       " 289: 'Nincada',\n",
       " 290: 'Ninjask',\n",
       " 291: 'Shedinja',\n",
       " 292: 'Whismur',\n",
       " 293: 'Loudred',\n",
       " 294: 'Exploud',\n",
       " 295: 'Makuhita',\n",
       " 296: 'Hariyama',\n",
       " 297: 'Azurill',\n",
       " 298: 'Nosepass',\n",
       " 299: 'Skitty',\n",
       " 300: 'Delcatty',\n",
       " 301: 'Sableye',\n",
       " 302: 'Mawile',\n",
       " 303: 'Aron',\n",
       " 304: 'Lairon',\n",
       " 305: 'Aggron',\n",
       " 306: 'Meditite',\n",
       " 307: 'Medicham',\n",
       " 308: 'Electrike',\n",
       " 309: 'Manectric',\n",
       " 310: 'Plusle',\n",
       " 311: 'Minun',\n",
       " 312: 'Volbeat',\n",
       " 313: 'Illumise',\n",
       " 314: 'Roselia',\n",
       " 315: 'Gulpin',\n",
       " 316: 'Swalot',\n",
       " 317: 'Carvanha',\n",
       " 318: 'Sharpedo',\n",
       " 319: 'Wailmer',\n",
       " 320: 'Wailord',\n",
       " 321: 'Numel',\n",
       " 322: 'Camerupt',\n",
       " 323: 'Torkoal',\n",
       " 324: 'Spoink',\n",
       " 325: 'Grumpig',\n",
       " 326: 'Spinda',\n",
       " 327: 'Trapinch',\n",
       " 328: 'Vibrava',\n",
       " 329: 'Flygon',\n",
       " 330: 'Cacnea',\n",
       " 331: 'Cacturne',\n",
       " 332: 'Swablu',\n",
       " 333: 'Altaria',\n",
       " 334: 'Zangoose',\n",
       " 335: 'Seviper',\n",
       " 336: 'Lunatone',\n",
       " 337: 'Solrock',\n",
       " 338: 'Barboach',\n",
       " 339: 'Whiscash',\n",
       " 340: 'Corphish',\n",
       " 341: 'Crawdaunt',\n",
       " 342: 'Baltoy',\n",
       " 343: 'Claydol',\n",
       " 344: 'Lileep',\n",
       " 345: 'Cradily',\n",
       " 346: 'Anorith',\n",
       " 347: 'Armaldo',\n",
       " 348: 'Feebas',\n",
       " 349: 'Milotic',\n",
       " 350: 'Castform',\n",
       " 351: 'Kecleon',\n",
       " 352: 'Shuppet',\n",
       " 353: 'Banette',\n",
       " 354: 'Duskull',\n",
       " 355: 'Dusclops',\n",
       " 356: 'Tropius',\n",
       " 357: 'Chimecho',\n",
       " 358: 'Absol',\n",
       " 359: 'Wynaut',\n",
       " 360: 'Snorunt',\n",
       " 361: 'Glalie',\n",
       " 362: 'Spheal',\n",
       " 363: 'Sealeo',\n",
       " 364: 'Walrein',\n",
       " 365: 'Clamperl',\n",
       " 366: 'Huntail',\n",
       " 367: 'Gorebyss',\n",
       " 368: 'Relicanth',\n",
       " 369: 'Luvdisc',\n",
       " 370: 'Bagon',\n",
       " 371: 'Shelgon',\n",
       " 372: 'Salamence',\n",
       " 373: 'Beldum',\n",
       " 374: 'Metang',\n",
       " 375: 'Metagross',\n",
       " 376: 'Regirock',\n",
       " 377: 'Regice',\n",
       " 378: 'Registeel',\n",
       " 379: 'Latias',\n",
       " 380: 'Latios',\n",
       " 381: 'Kyogre',\n",
       " 382: 'Groudon',\n",
       " 383: 'Rayquaza',\n",
       " 384: 'Jirachi',\n",
       " 385: 'Deoxys',\n",
       " 386: 'Turtwig',\n",
       " 387: 'Grotle',\n",
       " 388: 'Torterra',\n",
       " 389: 'Chimchar',\n",
       " 390: 'Monferno',\n",
       " 391: 'Infernape',\n",
       " 392: 'Piplup',\n",
       " 393: 'Prinplup',\n",
       " 394: 'Empoleon',\n",
       " 395: 'Starly',\n",
       " 396: 'Staravia',\n",
       " 397: 'Staraptor',\n",
       " 398: 'Bidoof',\n",
       " 399: 'Bibarel',\n",
       " 400: 'Kricketot',\n",
       " 401: 'Kricketune',\n",
       " 402: 'Shinx',\n",
       " 403: 'Luxio',\n",
       " 404: 'Luxray',\n",
       " 405: 'Budew',\n",
       " 406: 'Roserade',\n",
       " 407: 'Cranidos',\n",
       " 408: 'Rampardos',\n",
       " 409: 'Shieldon',\n",
       " 410: 'Bastiodon',\n",
       " 411: 'Burmy',\n",
       " 412: 'Wormadam',\n",
       " 413: 'Mothim',\n",
       " 414: 'Combee',\n",
       " 415: 'Vespiquen',\n",
       " 416: 'Pachirisu',\n",
       " 417: 'Buizel',\n",
       " 418: 'Floatzel',\n",
       " 419: 'Cherubi',\n",
       " 420: 'Cherrim',\n",
       " 421: 'Shellos',\n",
       " 422: 'Gastrodon',\n",
       " 423: 'Ambipom',\n",
       " 424: 'Drifloon',\n",
       " 425: 'Drifblim',\n",
       " 426: 'Buneary',\n",
       " 427: 'Lopunny',\n",
       " 428: 'Mismagius',\n",
       " 429: 'Honchkrow',\n",
       " 430: 'Glameow',\n",
       " 431: 'Purugly',\n",
       " 432: 'Chingling',\n",
       " 433: 'Stunky',\n",
       " 434: 'Skuntank',\n",
       " 435: 'Bronzor',\n",
       " 436: 'Bronzong',\n",
       " 437: 'Bonsly',\n",
       " 438: 'Mime Jr.',\n",
       " 439: 'Happiny',\n",
       " 440: 'Chatot',\n",
       " 441: 'Spiritomb',\n",
       " 442: 'Gible',\n",
       " 443: 'Gabite',\n",
       " 444: 'Garchomp',\n",
       " 445: 'Munchlax',\n",
       " 446: 'Riolu',\n",
       " 447: 'Lucario',\n",
       " 448: 'Hippopotas',\n",
       " 449: 'Hippowdon',\n",
       " 450: 'Skorupi',\n",
       " 451: 'Drapion',\n",
       " 452: 'Croagunk',\n",
       " 453: 'Toxicroak',\n",
       " 454: 'Carnivine',\n",
       " 455: 'Finneon',\n",
       " 456: 'Lumineon',\n",
       " 457: 'Mantyke',\n",
       " 458: 'Snover',\n",
       " 459: 'Abomasnow',\n",
       " 460: 'Weavile',\n",
       " 461: 'Magnezone',\n",
       " 462: 'Lickilicky',\n",
       " 463: 'Rhyperior',\n",
       " 464: 'Tangrowth',\n",
       " 465: 'Electivire',\n",
       " 466: 'Magmortar',\n",
       " 467: 'Togekiss',\n",
       " 468: 'Yanmega',\n",
       " 469: 'Leafeon',\n",
       " 470: 'Glaceon',\n",
       " 471: 'Gliscor',\n",
       " 472: 'Mamoswine',\n",
       " 473: 'Porygon-Z',\n",
       " 474: 'Gallade',\n",
       " 475: 'Probopass',\n",
       " 476: 'Dusknoir',\n",
       " 477: 'Froslass',\n",
       " 478: 'Rotom',\n",
       " 479: 'Uxie',\n",
       " 480: 'Mesprit',\n",
       " 481: 'Azelf',\n",
       " 482: 'Dialga',\n",
       " 483: 'Palkia',\n",
       " 484: 'Heatran',\n",
       " 485: 'Regigigas',\n",
       " 486: 'Giratina',\n",
       " 487: 'Cresselia',\n",
       " 488: 'Phione',\n",
       " 489: 'Manaphy',\n",
       " 490: 'Darkrai',\n",
       " 491: 'Shaymin',\n",
       " 492: 'Arceus',\n",
       " 493: 'Victini',\n",
       " 494: 'Snivy',\n",
       " 495: 'Servine',\n",
       " 496: 'Serperior',\n",
       " 497: 'Tepig',\n",
       " 498: 'Pignite',\n",
       " 499: 'Emboar',\n",
       " 500: 'Oshawott',\n",
       " 501: 'Dewott',\n",
       " 502: 'Samurott',\n",
       " 503: 'Patrat',\n",
       " 504: 'Watchog',\n",
       " 505: 'Lillipup',\n",
       " 506: 'Herdier',\n",
       " 507: 'Stoutland',\n",
       " 508: 'Purrloin',\n",
       " 509: 'Liepard',\n",
       " 510: 'Pansage',\n",
       " 511: 'Simisage',\n",
       " 512: 'Pansear',\n",
       " 513: 'Simisear',\n",
       " 514: 'Panpour',\n",
       " 515: 'Simipour',\n",
       " 516: 'Munna',\n",
       " 517: 'Musharna',\n",
       " 518: 'Pidove',\n",
       " 519: 'Tranquill',\n",
       " 520: 'Unfezant',\n",
       " 521: 'Blitzle',\n",
       " 522: 'Zebstrika',\n",
       " 523: 'Roggenrola',\n",
       " 524: 'Boldore',\n",
       " 525: 'Gigalith',\n",
       " 526: 'Woobat',\n",
       " 527: 'Swoobat',\n",
       " 528: 'Drilbur',\n",
       " 529: 'Excadrill',\n",
       " 530: 'Audino',\n",
       " 531: 'Timburr',\n",
       " 532: 'Gurdurr',\n",
       " 533: 'Conkeldurr',\n",
       " 534: 'Tympole',\n",
       " 535: 'Palpitoad',\n",
       " 536: 'Seismitoad',\n",
       " 537: 'Throh',\n",
       " 538: 'Sawk',\n",
       " 539: 'Sewaddle',\n",
       " 540: 'Swadloon',\n",
       " 541: 'Leavanny',\n",
       " 542: 'Venipede',\n",
       " 543: 'Whirlipede',\n",
       " 544: 'Scolipede',\n",
       " 545: 'Cottonee',\n",
       " 546: 'Whimsicott',\n",
       " 547: 'Petilil',\n",
       " 548: 'Lilligant',\n",
       " 549: 'Basculin',\n",
       " 550: 'Sandile',\n",
       " 551: 'Krokorok',\n",
       " 552: 'Krookodile',\n",
       " 553: 'Darumaka',\n",
       " 554: 'Darmanitan',\n",
       " 555: 'Maractus',\n",
       " 556: 'Dwebble',\n",
       " 557: 'Crustle',\n",
       " 558: 'Scraggy',\n",
       " 559: 'Scrafty',\n",
       " 560: 'Sigilyph',\n",
       " 561: 'Yamask',\n",
       " 562: 'Cofagrigus',\n",
       " 563: 'Tirtouga',\n",
       " 564: 'Carracosta',\n",
       " 565: 'Archen',\n",
       " 566: 'Archeops',\n",
       " 567: 'Trubbish',\n",
       " 568: 'Garbodor',\n",
       " 569: 'Zorua',\n",
       " 570: 'Zoroark',\n",
       " 571: 'Minccino',\n",
       " 572: 'Cinccino',\n",
       " 573: 'Gothita',\n",
       " 574: 'Gothorita',\n",
       " 575: 'Gothitelle',\n",
       " 576: 'Solosis',\n",
       " 577: 'Duosion',\n",
       " 578: 'Reuniclus',\n",
       " 579: 'Ducklett',\n",
       " 580: 'Swanna',\n",
       " 581: 'Vanillite',\n",
       " 582: 'Vanillish',\n",
       " 583: 'Vanilluxe',\n",
       " 584: 'Deerling',\n",
       " 585: 'Sawsbuck',\n",
       " 586: 'Emolga',\n",
       " 587: 'Karrablast',\n",
       " 588: 'Escavalier',\n",
       " 589: 'Foongus',\n",
       " 590: 'Amoonguss',\n",
       " 591: 'Frillish',\n",
       " 592: 'Jellicent',\n",
       " 593: 'Alomomola',\n",
       " 594: 'Joltik',\n",
       " 595: 'Galvantula',\n",
       " 596: 'Ferroseed',\n",
       " 597: 'Ferrothorn',\n",
       " 598: 'Klink',\n",
       " 599: 'Klang',\n",
       " 600: 'Klinklang',\n",
       " 601: 'Tynamo',\n",
       " 602: 'Eelektrik',\n",
       " 603: 'Eelektross',\n",
       " 604: 'Elgyem',\n",
       " 605: 'Beheeyem',\n",
       " 606: 'Litwick',\n",
       " 607: 'Lampent',\n",
       " 608: 'Chandelure',\n",
       " 609: 'Axew',\n",
       " 610: 'Fraxure',\n",
       " 611: 'Haxorus',\n",
       " 612: 'Cubchoo',\n",
       " 613: 'Beartic',\n",
       " 614: 'Cryogonal',\n",
       " 615: 'Shelmet',\n",
       " 616: 'Accelgor',\n",
       " 617: 'Stunfisk',\n",
       " 618: 'Mienfoo',\n",
       " 619: 'Mienshao',\n",
       " 620: 'Druddigon',\n",
       " 621: 'Golett',\n",
       " 622: 'Golurk',\n",
       " 623: 'Pawniard',\n",
       " 624: 'Bisharp',\n",
       " 625: 'Bouffalant',\n",
       " 626: 'Rufflet',\n",
       " 627: 'Braviary',\n",
       " 628: 'Vullaby',\n",
       " 629: 'Mandibuzz',\n",
       " 630: 'Heatmor',\n",
       " 631: 'Durant',\n",
       " 632: 'Deino',\n",
       " 633: 'Zweilous',\n",
       " 634: 'Hydreigon',\n",
       " 635: 'Larvesta',\n",
       " 636: 'Volcarona',\n",
       " 637: 'Cobalion',\n",
       " 638: 'Terrakion',\n",
       " 639: 'Virizion',\n",
       " 640: 'Tornadus',\n",
       " 641: 'Thundurus',\n",
       " 642: 'Reshiram',\n",
       " 643: 'Zekrom',\n",
       " 644: 'Landorus',\n",
       " 645: 'Kyurem',\n",
       " 646: 'Keldeo',\n",
       " 647: 'Meloetta',\n",
       " 648: 'Genesect',\n",
       " 649: 'Chespin',\n",
       " 650: 'Quilladin',\n",
       " 651: 'Chesnaught',\n",
       " 652: 'Fennekin',\n",
       " 653: 'Braixen',\n",
       " 654: 'Delphox',\n",
       " 655: 'Froakie',\n",
       " 656: 'Frogadier',\n",
       " 657: 'Greninja',\n",
       " 658: 'Bunnelby',\n",
       " 659: 'Diggersby',\n",
       " 660: 'Fletchling',\n",
       " 661: 'Fletchinder',\n",
       " 662: 'Talonflame',\n",
       " 663: 'Scatterbug',\n",
       " 664: 'Spewpa',\n",
       " 665: 'Vivillon',\n",
       " 666: 'Litleo',\n",
       " 667: 'Pyroar',\n",
       " 668: 'Flabébé',\n",
       " 669: 'Floette',\n",
       " 670: 'Florges',\n",
       " 671: 'Skiddo',\n",
       " 672: 'Gogoat',\n",
       " 673: 'Pancham',\n",
       " 674: 'Pangoro',\n",
       " 675: 'Furfrou',\n",
       " 676: 'Espurr',\n",
       " 677: 'Meowstic',\n",
       " 678: 'Honedge',\n",
       " 679: 'Doublade',\n",
       " 680: 'Aegislash',\n",
       " 681: 'Spritzee',\n",
       " 682: 'Aromatisse',\n",
       " 683: 'Swirlix',\n",
       " 684: 'Slurpuff',\n",
       " 685: 'Inkay',\n",
       " 686: 'Malamar',\n",
       " 687: 'Binacle',\n",
       " 688: 'Barbaracle',\n",
       " 689: 'Skrelp',\n",
       " 690: 'Dragalge',\n",
       " 691: 'Clauncher',\n",
       " 692: 'Clawitzer',\n",
       " 693: 'Helioptile',\n",
       " 694: 'Heliolisk',\n",
       " 695: 'Tyrunt',\n",
       " 696: 'Tyrantrum',\n",
       " 697: 'Amaura',\n",
       " 698: 'Aurorus',\n",
       " 699: 'Sylveon',\n",
       " 700: 'Hawlucha',\n",
       " 701: 'Dedenne',\n",
       " 702: 'Carbink',\n",
       " 703: 'Goomy',\n",
       " 704: 'Sliggoo',\n",
       " 705: 'Goodra',\n",
       " 706: 'Klefki',\n",
       " 707: 'Phantump',\n",
       " 708: 'Trevenant',\n",
       " 709: 'Pumpkaboo',\n",
       " 710: 'Gourgeist',\n",
       " 711: 'Bergmite',\n",
       " 712: 'Avalugg',\n",
       " 713: 'Noibat',\n",
       " 714: 'Noivern',\n",
       " 715: 'Xerneas',\n",
       " 716: 'Yveltal',\n",
       " 717: 'Zygarde',\n",
       " 718: 'Diancie',\n",
       " 719: 'Hoopa',\n",
       " 720: 'Volcanion'}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(pokemon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0da09716",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pokemon)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cfed432",
   "metadata": {},
   "source": [
    "### <font color=blue> sort_values method </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "28f5c627",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "459    Abomasnow\n",
       "62          Abra\n",
       "358        Absol\n",
       "616     Accelgor\n",
       "680    Aegislash\n",
       "Name: Pokemon, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon.sort_values().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "95d60f48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "717     Zygarde\n",
       "633    Zweilous\n",
       "40        Zubat\n",
       "569       Zorua\n",
       "570     Zoroark\n",
       "Name: Pokemon, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon.sort_values(ascending = False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d86f388a",
   "metadata": {},
   "source": [
    "### <font color=blue> sort_index method </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "41c489d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type\n",
       "Grass       Bulbasaur\n",
       "Grass         Ivysaur\n",
       "Grass        Venusaur\n",
       "Fire       Charmander\n",
       "Fire       Charmeleon\n",
       "              ...    \n",
       "Dark          Yveltal\n",
       "Dragon        Zygarde\n",
       "Rock          Diancie\n",
       "Psychic         Hoopa\n",
       "Fire        Volcanion\n",
       "Name: Pokemon, Length: 721, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poke_idx = pd.read_csv(\"./datasets/pokemon.csv\", index_col = \"Type\").squeeze(\"columns\")\n",
    "poke_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "40022b1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type\n",
       "Bug        Leavanny\n",
       "Bug           Burmy\n",
       "Bug       Scolipede\n",
       "Bug        Genesect\n",
       "Bug      Kricketune\n",
       "            ...    \n",
       "Water       Panpour\n",
       "Water          Seel\n",
       "Water       Dewgong\n",
       "Water      Politoed\n",
       "Water      Chinchou\n",
       "Name: Pokemon, Length: 721, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poke_idx.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "705c0e44",
   "metadata": {},
   "source": [
    "### <font color=blue> Check for inclusion using 'in' keyword </font>\n",
    "\n",
    "* By default Pandas will look with index while using the 'in' keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bfc2973b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Bulbasaur\n",
       "1       Ivysaur\n",
       "2      Venusaur\n",
       "3    Charmander\n",
       "4    Charmeleon\n",
       "Name: Pokemon, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b7155892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Bulbasaur' in pokemon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a033148f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4 in pokemon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c620c44e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4 in pokemon.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d376041a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Bulbasaur' in pokemon.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3304818e",
   "metadata": {},
   "source": [
    "### <font color=blue> Extract Series value by Indexing </font>\n",
    "\n",
    "* Negative indexing doesn't work in Pandas like `sr[-1]`, `sr[-20]`, but negative slicig is possible like `sr[-20 : -10]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d5d75838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Bulbasaur\n",
       "1       Ivysaur\n",
       "2      Venusaur\n",
       "3    Charmander\n",
       "4    Charmeleon\n",
       "Name: Pokemon, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b40105aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Charmander'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b3174eab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Bulbasaur\n",
       "1      Ivysaur\n",
       "Name: Pokemon, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "90aedbbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Bulbasaur\n",
       "2     Venusaur\n",
       "Name: Pokemon, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon[: 4 : 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9f67f9e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "716    Yveltal\n",
       "717    Zygarde\n",
       "718    Diancie\n",
       "719      Hoopa\n",
       "Name: Pokemon, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pokemon[-1] error\n",
    "\n",
    "pokemon[-5 : -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "55e5e9e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "716      Yveltal\n",
       "717      Zygarde\n",
       "718      Diancie\n",
       "719        Hoopa\n",
       "720    Volcanion\n",
       "Name: Pokemon, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon[-5 :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9006342f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1        Ivysaur\n",
       "2       Venusaur\n",
       "720    Volcanion\n",
       "Name: Pokemon, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon[[1, 2, 720]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6721acb9",
   "metadata": {},
   "source": [
    "### <font color=blue> Extract Series Value by Index </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2e790472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type\n",
       "Grass     Bulbasaur\n",
       "Grass       Ivysaur\n",
       "Grass      Venusaur\n",
       "Fire     Charmander\n",
       "Fire     Charmeleon\n",
       "Name: Pokemon, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poke_idx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a01f3c11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Charmander'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poke_idx[\"Fire\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "27839d46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type\n",
       "Grass    Bulbasaur\n",
       "Grass      Ivysaur\n",
       "Name: Pokemon, dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poke_idx[\"Grass\"].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "59911d4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ivysaur'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poke_idx[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b84cfccd",
   "metadata": {},
   "source": [
    "### <font color=blue> get method </font>\n",
    "\n",
    "* Provides advantage wrt indexing, as we can provide fallback value ie. if value not present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "10d38537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Charmander'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon.get(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "76c9de92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type\n",
       "Grass     Bulbasaur\n",
       "Grass       Ivysaur\n",
       "Grass      Venusaur\n",
       "Grass        Oddish\n",
       "Grass         Gloom\n",
       "            ...    \n",
       "Grass       Chespin\n",
       "Grass     Quilladin\n",
       "Grass    Chesnaught\n",
       "Grass        Skiddo\n",
       "Grass        Gogoat\n",
       "Name: Pokemon, Length: 66, dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poke_idx.get(\"Grass\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a9842fab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'N/A'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poke_idx.get(\"Nor-World\", default = \"N/A\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ac548073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'N/A'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if any of the index not present in the series we will get default value\n",
    "\n",
    "poke_idx.get([\"Grass\", \"Nor-World\"], default = \"N/A\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8d83a88f",
   "metadata": {},
   "outputs": [],
   "source": [
    "del(poke_idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "610c06c3",
   "metadata": {},
   "source": [
    "### <font color=blue> Overwrite a Series Value </font>\n",
    "\n",
    "* If overwrite index is not present, it will be created and append to the Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "cb544f9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pokemon\n",
       "Bulbasaur       Grass\n",
       "Ivysaur         Grass\n",
       "Venusaur        Grass\n",
       "Charmander       Fire\n",
       "Charmeleon       Fire\n",
       "               ...   \n",
       "Yveltal          Dark\n",
       "Zygarde        Dragon\n",
       "Diancie          Rock\n",
       "Hoopa         Psychic\n",
       "Volcanion        Fire\n",
       "Name: Type, Length: 721, dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr = pd.read_csv(\"./datasets/pokemon.csv\", usecols=[\"Pokemon\", \"Type\"], index_col=\"Pokemon\").squeeze(\"columns\")\n",
    "sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a6207c9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pokemon\n",
       "Bulbasaur     Water\n",
       "Ivysaur       Grass\n",
       "Venusaur      Grass\n",
       "Charmander     Fire\n",
       "Charmeleon     Fire\n",
       "Name: Type, dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr[\"Bulbasaur\"] = \"Water\"\n",
    "sr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2488e6d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pokemon\n",
       "Bulbasaur     Shadow\n",
       "Ivysaur        Grass\n",
       "Venusaur      Shadow\n",
       "Charmander      Fire\n",
       "Charmeleon      Fire\n",
       "Name: Type, dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr[[0, 2]] = \"Shadow\"\n",
    "sr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "59e68d0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pokemon\n",
       "Bulbasaur       Shadow\n",
       "Ivysaur          Grass\n",
       "Venusaur        Shadow\n",
       "Charmander        Fire\n",
       "Charmeleon        Fire\n",
       "                ...   \n",
       "Zygarde         Dragon\n",
       "Diancie           Rock\n",
       "Hoopa          Psychic\n",
       "Volcanion         Fire\n",
       "Gauravior     Universe\n",
       "Name: Type, Length: 722, dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr[\"Gauravior\"] = \"Universe\"\n",
    "sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "161a8475",
   "metadata": {},
   "outputs": [],
   "source": [
    "del(sr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47e4ea87",
   "metadata": {},
   "source": [
    "### <font color=blue> copy method </font>\n",
    "\n",
    "* The copy() method returns a copy of the DataFrame.\n",
    "* By default, the copy is a \"deep copy\" meaning that any changes made in the original DataFrame will NOT be reflected in the copy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f87f2845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pokemon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bulbasaur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ivysaur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Venusaur</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pokemon\n",
       "0  Bulbasaur\n",
       "1    Ivysaur\n",
       "2   Venusaur"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon_df = pd.read_csv(\"./datasets/pokemon.csv\", usecols=[\"Pokemon\"])\n",
    "pokemon_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "fc1b94f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Bulbasaur\n",
       "1      Ivysaur\n",
       "2     Venusaur\n",
       "Name: Pokemon, dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon_ser = pokemon_df.squeeze(\"columns\")\n",
    "pokemon_ser.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7e99c0f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Change\n",
       "Name: Pokemon, dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon_ser[0] = \"Change\"\n",
    "pokemon_ser.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c9fc3df7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pokemon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Change</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Pokemon\n",
       "0  Change"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "64c858ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Bulbasaur\n",
       "Name: Pokemon, dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to prevent this side-effect, we use copy method\n",
    "del(pokemon_ser)\n",
    "pokemon_df = pd.read_csv(\"./datasets/pokemon.csv\", usecols=[\"Pokemon\"])\n",
    "pokemon_ser = pokemon_df.squeeze(\"columns\").copy()\n",
    "pokemon_ser.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f8602e31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Let's change\n",
       "Name: Pokemon, dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon_ser[0] = \"Let's change\"\n",
    "pokemon_ser.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1af8f3dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pokemon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bulbasaur</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pokemon\n",
       "0  Bulbasaur"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0a7855b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "del(pokemon_df)\n",
    "del(pokemon_ser)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "089c39eb",
   "metadata": {},
   "source": [
    "### <font color=blue> Broadcasting </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d81db398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Stock Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>54.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Stock Price\n",
       "0        50.12\n",
       "1        54.10\n",
       "2        54.65"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "google = pd.read_csv(\"./datasets/google_stock_price.csv\")\n",
    "google.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0d20c2a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Stock Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Stock Price\n",
       "0        60.12\n",
       "1        64.10\n",
       "2        64.65"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# just like we see broadcasting in Numpy\n",
    "\n",
    "google = google + 10\n",
    "google.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e3e80811",
   "metadata": {},
   "outputs": [],
   "source": [
    "del(google) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3607255",
   "metadata": {},
   "source": [
    "### <font color=blue> value_counts method </font>\n",
    "\n",
    "* Return a Series containing counts of unique values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e84a6ece",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pokemon\n",
       "Bulbasaur     Grass\n",
       "Ivysaur       Grass\n",
       "Venusaur      Grass\n",
       "Charmander     Fire\n",
       "Charmeleon     Fire\n",
       "Name: Type, dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poke_idx = pd.read_csv(\"./datasets/pokemon.csv\", index_col=\"Pokemon\").squeeze(\"columns\")\n",
    "poke_idx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "416d2f1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Water       105\n",
       "Normal       93\n",
       "Grass        66\n",
       "Bug          63\n",
       "Fire         47\n",
       "Psychic      47\n",
       "Rock         41\n",
       "Electric     36\n",
       "Ground       30\n",
       "Poison       28\n",
       "Dark         28\n",
       "Fighting     25\n",
       "Dragon       24\n",
       "Ghost        23\n",
       "Ice          23\n",
       "Steel        22\n",
       "Fairy        17\n",
       "Flying        3\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poke_idx.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0de32477",
   "metadata": {},
   "source": [
    "### <font color=blue> apply method </font>\n",
    "\n",
    "* Invoke a specified function on every value of Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "9ef593ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pokemon\n",
       "Bulbasaur     Grass\n",
       "Ivysaur       Grass\n",
       "Venusaur      Grass\n",
       "Charmander     Fire\n",
       "Charmeleon     Fire\n",
       "Name: Type, dtype: object"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poke_idx.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6480a007",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rank_pokemon(pokemon_type):\n",
    "    if pokemon_type in [\"Grass\", \"Bug\"]:\n",
    "        return \"Classic\"\n",
    "    else:\n",
    "        return \"Unique\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "796aceb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pokemon\n",
       "Bulbasaur     Classic\n",
       "Ivysaur       Classic\n",
       "Venusaur      Classic\n",
       "Charmander     Unique\n",
       "Charmeleon     Unique\n",
       "Name: Type, dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poke_idx.apply(rank_pokemon).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45e6cdd4",
   "metadata": {},
   "source": [
    "### <font color=blue> map method </font>\n",
    "* Map values of Series according to an input mapping or function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "b451cefc",
   "metadata": {},
   "outputs": [],
   "source": [
    "mappings = {\n",
    "    \"Grass\": \"Classic\",\n",
    "    \"Water\": \"Classic\",\n",
    "    \"Fire\": \"Classic\",\n",
    "    \"Dark\": \"Unique\",\n",
    "    \"Psychic\": \"Unique\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "171bbe74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pokemon\n",
       "Bulbasaur     Grass\n",
       "Ivysaur       Grass\n",
       "Venusaur      Grass\n",
       "Charmander     Fire\n",
       "Charmeleon     Fire\n",
       "Name: Type, dtype: object"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poke_idx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "4008e2cd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pokemon\n",
       "Bulbasaur     Classic\n",
       "Ivysaur       Classic\n",
       "Venusaur      Classic\n",
       "Charmander    Classic\n",
       "Charmeleon    Classic\n",
       "               ...   \n",
       "Yveltal        Unique\n",
       "Zygarde           NaN\n",
       "Diancie           NaN\n",
       "Hoopa          Unique\n",
       "Volcanion     Classic\n",
       "Name: Type, Length: 721, dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poke_idx.map(mappings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b840f656",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Grass      Classic\n",
       "Water      Classic\n",
       "Fire       Classic\n",
       "Dark        Unique\n",
       "Psychic     Unique\n",
       "dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can also pass series in mapping\n",
    "\n",
    "mappings_ser = pd.Series(mappings)\n",
    "mappings_ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "4d8b9e16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pokemon\n",
       "Bulbasaur     Classic\n",
       "Ivysaur       Classic\n",
       "Venusaur      Classic\n",
       "Charmander    Classic\n",
       "Charmeleon    Classic\n",
       "               ...   \n",
       "Yveltal        Unique\n",
       "Zygarde           NaN\n",
       "Diancie           NaN\n",
       "Hoopa          Unique\n",
       "Volcanion     Classic\n",
       "Name: Type, Length: 721, dtype: object"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poke_idx.map(mappings_ser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8ef7ac4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "del(mappings)\n",
    "del(mappings_ser)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c568689d",
   "metadata": {},
   "source": [
    "## <font color=#fe7401> DataFrames </font>\n",
    "\n",
    "* A Pandas DataFrame is a 2 dimensional data structure, like a 2 dimensional array, or a table with rows and columns."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a60d96b",
   "metadata": {},
   "source": [
    "### <font color=blue> Knowing a Series/DataFrame </font>\n",
    "\n",
    "\n",
    "* It is possible to understand a Series better by using `describe` method.\n",
    "* The method provides details like mean, std, etc. about a series.\n",
    "* Two methods majorly `info` and `describe` can be used to know about the data, present in a data frame.\n",
    "* `describe` method by default provides details of only numeric fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "35a9b7f6",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>rain</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31.043461</td>\n",
       "      <td>94.706951</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.293718</td>\n",
       "      <td>40.016916</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49.014004</td>\n",
       "      <td>60.646450</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29.672352</td>\n",
       "      <td>107.967292</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.313781</td>\n",
       "      <td>138.697785</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16.647243</td>\n",
       "      <td>187.910014</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13.658539</td>\n",
       "      <td>150.717186</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26.591610</td>\n",
       "      <td>102.786701</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>33.228899</td>\n",
       "      <td>127.169316</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16.487881</td>\n",
       "      <td>128.133249</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        temp        rain location\n",
       "0  31.043461   94.706951        A\n",
       "1  24.293718   40.016916        A\n",
       "2  49.014004   60.646450        A\n",
       "3  29.672352  107.967292        A\n",
       "4  22.313781  138.697785        A\n",
       "5  16.647243  187.910014        B\n",
       "6  13.658539  150.717186        B\n",
       "7  26.591610  102.786701        B\n",
       "8  33.228899  127.169316        B\n",
       "9  16.487881  128.133249        B"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'temp':pd.Series(28 + 10*np.random.randn(10)), \n",
    "                'rain':pd.Series(100 + 50*np.random.randn(10)),\n",
    "             'location':list('AAAAABBBBB')})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "82cde920",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   temp      10 non-null     float64\n",
      " 1   rain      10 non-null     float64\n",
      " 2   location  10 non-null     object \n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 368.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "73cd4cc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26.295149</td>\n",
       "      <td>113.875186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.365909</td>\n",
       "      <td>43.009427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>13.658539</td>\n",
       "      <td>40.016916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18.063877</td>\n",
       "      <td>96.726888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>25.442664</td>\n",
       "      <td>117.568304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>30.700684</td>\n",
       "      <td>136.056651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>49.014004</td>\n",
       "      <td>187.910014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temp        rain\n",
       "count  10.000000   10.000000\n",
       "mean   26.295149  113.875186\n",
       "std    10.365909   43.009427\n",
       "min    13.658539   40.016916\n",
       "25%    18.063877   96.726888\n",
       "50%    25.442664  117.568304\n",
       "75%    30.700684  136.056651\n",
       "max    49.014004  187.910014"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d4e57a92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       location\n",
       "count        10\n",
       "unique        2\n",
       "top           A\n",
       "freq          5"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use include argument to white list data types that has to be included in the result.\n",
    "# below is giving result for col, `location`\n",
    "\n",
    "df.describe(include=['object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c08db30",
   "metadata": {},
   "source": [
    "### <font color=blue> Methods and Attributes b/w Series and DataFrames </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e0bbde18",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Butler</td>\n",
       "      <td>2433333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Raul Neto</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>25.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-1</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>Tibor Pleiss</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>21.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-3</td>\n",
       "      <td>256.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>458 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name            Team  Number Position   Age Height  Weight  \\\n",
       "0    Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1      Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2     John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3      R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4    Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "..             ...             ...     ...      ...   ...    ...     ...   \n",
       "453   Shelvin Mack       Utah Jazz     8.0       PG  26.0    6-3   203.0   \n",
       "454      Raul Neto       Utah Jazz    25.0       PG  24.0    6-1   179.0   \n",
       "455   Tibor Pleiss       Utah Jazz    21.0        C  26.0    7-3   256.0   \n",
       "456    Jeff Withey       Utah Jazz    24.0        C  26.0    7-0   231.0   \n",
       "457            NaN             NaN     NaN      NaN   NaN    NaN     NaN   \n",
       "\n",
       "               College     Salary  \n",
       "0                Texas  7730337.0  \n",
       "1            Marquette  6796117.0  \n",
       "2    Boston University        NaN  \n",
       "3        Georgia State  1148640.0  \n",
       "4                  NaN  5000000.0  \n",
       "..                 ...        ...  \n",
       "453             Butler  2433333.0  \n",
       "454                NaN   900000.0  \n",
       "455                NaN  2900000.0  \n",
       "456             Kansas   947276.0  \n",
       "457                NaN        NaN  \n",
       "\n",
       "[458 rows x 9 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba = pd.read_csv(\"./datasets/nba.csv\")\n",
    "nba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "52e28b64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    11\n",
       "1    22\n",
       "2    33\n",
       "3    44\n",
       "4    55\n",
       "dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([11, 22, 33, 44, 55])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "333729a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    11\n",
       "1    22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "3c3278b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name       Team  Number Position   Age Height  Weight College  \\\n",
       "456  Jeff Withey  Utah Jazz    24.0        C  26.0    7-0   231.0  Kansas   \n",
       "457          NaN        NaN     NaN      NaN   NaN    NaN     NaN     NaN   \n",
       "\n",
       "       Salary  \n",
       "456  947276.0  \n",
       "457       NaN  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "251358c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5, step=1)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "352b32df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=458, step=1)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "43f9c0b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11, 22, 33, 44, 55], dtype=int64)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1e606e9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Avery Bradley', 'Boston Celtics', 0.0, ..., 180.0, 'Texas',\n",
       "        7730337.0],\n",
       "       ['Jae Crowder', 'Boston Celtics', 99.0, ..., 235.0, 'Marquette',\n",
       "        6796117.0],\n",
       "       ['John Holland', 'Boston Celtics', 30.0, ..., 205.0,\n",
       "        'Boston University', nan],\n",
       "       ...,\n",
       "       ['Tibor Pleiss', 'Utah Jazz', 21.0, ..., 256.0, nan, 2900000.0],\n",
       "       ['Jeff Withey', 'Utah Jazz', 24.0, ..., 231.0, 'Kansas', 947276.0],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=object)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "a43c6097",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "a8ee39c1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(458, 9)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f4a11303",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "d62d586d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "d8cb479d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name         object\n",
       "Team         object\n",
       "Number      float64\n",
       "Position     object\n",
       "Age         float64\n",
       "Height       object\n",
       "Weight      float64\n",
       "College      object\n",
       "Salary      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nba.dtype -> Error\n",
    "\n",
    "nba.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "d624d11f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Team', 'Number', 'Position', 'Age', 'Height', 'Weight',\n",
       "       'College', 'Salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# s.columns -> Error\n",
    "\n",
    "nba.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "0b7ded91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 5 entries, 0 to 4\n",
      "Series name: None\n",
      "Non-Null Count  Dtype\n",
      "--------------  -----\n",
      "5 non-null      int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 168.0 bytes\n"
     ]
    }
   ],
   "source": [
    "s.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "ea6780d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 458 entries, 0 to 457\n",
      "Data columns (total 9 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Name      457 non-null    object \n",
      " 1   Team      457 non-null    object \n",
      " 2   Number    457 non-null    float64\n",
      " 3   Position  457 non-null    object \n",
      " 4   Age       457 non-null    float64\n",
      " 5   Height    457 non-null    object \n",
      " 6   Weight    457 non-null    float64\n",
      " 7   College   373 non-null    object \n",
      " 8   Salary    446 non-null    float64\n",
      "dtypes: float64(4), object(5)\n",
      "memory usage: 32.3+ KB\n"
     ]
    }
   ],
   "source": [
    "nba.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "e0078a45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     5.000000\n",
       "mean     33.000000\n",
       "std      17.392527\n",
       "min      11.000000\n",
       "25%      22.000000\n",
       "50%      33.000000\n",
       "75%      44.000000\n",
       "max      55.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "5f7867f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number</th>\n",
       "      <th>Age</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>457.000000</td>\n",
       "      <td>457.000000</td>\n",
       "      <td>457.000000</td>\n",
       "      <td>4.460000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17.678337</td>\n",
       "      <td>26.938731</td>\n",
       "      <td>221.522976</td>\n",
       "      <td>4.842684e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.966090</td>\n",
       "      <td>4.404016</td>\n",
       "      <td>26.368343</td>\n",
       "      <td>5.229238e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>3.088800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>1.044792e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>2.839073e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>6.500000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>307.000000</td>\n",
       "      <td>2.500000e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Number         Age      Weight        Salary\n",
       "count  457.000000  457.000000  457.000000  4.460000e+02\n",
       "mean    17.678337   26.938731  221.522976  4.842684e+06\n",
       "std     15.966090    4.404016   26.368343  5.229238e+06\n",
       "min      0.000000   19.000000  161.000000  3.088800e+04\n",
       "25%      5.000000   24.000000  200.000000  1.044792e+06\n",
       "50%     13.000000   26.000000  220.000000  2.839073e+06\n",
       "75%     25.000000   30.000000  240.000000  6.500000e+06\n",
       "max     99.000000   40.000000  307.000000  2.500000e+07"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "4154db91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[RangeIndex(start=0, stop=5, step=1)]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "3b18b421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[RangeIndex(start=0, stop=458, step=1),\n",
       " Index(['Name', 'Team', 'Number', 'Position', 'Age', 'Height', 'Weight',\n",
       "        'College', 'Salary'],\n",
       "       dtype='object')]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "42768806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hasnans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b700d7b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name        True\n",
       "Team        True\n",
       "Number      True\n",
       "Position    True\n",
       "Age         True\n",
       "Height      True\n",
       "Weight      True\n",
       "College     True\n",
       "Salary      True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nba.hasnans -> Error\n",
    "\n",
    "nba.isnull().any()    # DataFrame.isnull is an alias for DataFrame.isna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "a7d2708c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name Team  Number Position  Age Height  Weight College  Salary\n",
       "457  NaN  NaN     NaN      NaN  NaN    NaN     NaN     NaN     NaN"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "6c5707e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name         1\n",
       "Team         1\n",
       "Number       1\n",
       "Position     1\n",
       "Age          1\n",
       "Height       1\n",
       "Weight       1\n",
       "College     85\n",
       "Salary      12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6129634c",
   "metadata": {},
   "source": [
    "### <font color=blue> idxmax method </font>\n",
    "\n",
    "* Return index of first occurrence of maximum over requested axis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "a41b9a50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>96.677966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>96.697213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81.053520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>82.398728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>106.062393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>113.206976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>128.926721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>108.024799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>116.878853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>100.384439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Values\n",
       "0    96.677966\n",
       "1    96.697213\n",
       "2    81.053520\n",
       "3    82.398728\n",
       "4   106.062393\n",
       "..         ...\n",
       "95  113.206976\n",
       "96  128.926721\n",
       "97  108.024799\n",
       "98  116.878853\n",
       "99  100.384439\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_df = pd.DataFrame(data = np.random.normal(loc = 100, scale = 30, size = 100), columns = [\"Values\"])\n",
    "sample_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "60cea619",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Values    47\n",
       "dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# idxmin() complementary method\n",
    "\n",
    "sample_df.idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "0ccdc8a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>196.270627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>188.014668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Values\n",
       "47  196.270627\n",
       "30  188.014668"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_df.nlargest(n = 2, columns = \"Values\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ee42b72",
   "metadata": {},
   "source": [
    "### <font color=blue> Difference between shared methods </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "c2988876",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    11\n",
       "1    22\n",
       "2    33\n",
       "3    44\n",
       "4    55\n",
       "dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "beca273e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "165"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "a99def25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>New York</th>\n",
       "      <th>Los Angeles</th>\n",
       "      <th>Miami</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/16</td>\n",
       "      <td>985</td>\n",
       "      <td>122</td>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/16</td>\n",
       "      <td>738</td>\n",
       "      <td>788</td>\n",
       "      <td>534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/16</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/16</td>\n",
       "      <td>730</td>\n",
       "      <td>904</td>\n",
       "      <td>885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/16</td>\n",
       "      <td>114</td>\n",
       "      <td>71</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/16</td>\n",
       "      <td>936</td>\n",
       "      <td>502</td>\n",
       "      <td>497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/7/16</td>\n",
       "      <td>123</td>\n",
       "      <td>996</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/8/16</td>\n",
       "      <td>935</td>\n",
       "      <td>492</td>\n",
       "      <td>886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/9/16</td>\n",
       "      <td>846</td>\n",
       "      <td>954</td>\n",
       "      <td>823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1/10/16</td>\n",
       "      <td>54</td>\n",
       "      <td>285</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date  New York  Los Angeles  Miami\n",
       "0   1/1/16       985          122    499\n",
       "1   1/2/16       738          788    534\n",
       "2   1/3/16        14           20    933\n",
       "3   1/4/16       730          904    885\n",
       "4   1/5/16       114           71    253\n",
       "5   1/6/16       936          502    497\n",
       "6   1/7/16       123          996    115\n",
       "7   1/8/16       935          492    886\n",
       "8   1/9/16       846          954    823\n",
       "9  1/10/16        54          285    216"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rev = pd.read_csv(\"./datasets/revenue.csv\")\n",
    "rev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "62104678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "New York       5475\n",
       "Los Angeles    5134\n",
       "Miami          5641\n",
       "dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summing columns wise, default\n",
    "\n",
    "rev.sum(numeric_only = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "bf3060c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "New York       5475\n",
       "Los Angeles    5134\n",
       "Miami          5641\n",
       "dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rev.sum(axis = 0, numeric_only = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "63be3fe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "New York       5475\n",
       "Los Angeles    5134\n",
       "Miami          5641\n",
       "dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# more intituative way\n",
    "\n",
    "rev.sum(axis = \"index\", numeric_only = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "fe593ecb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1606\n",
       "1    2060\n",
       "2     967\n",
       "3    2519\n",
       "4     438\n",
       "5    1935\n",
       "6    1234\n",
       "7    2313\n",
       "8    2623\n",
       "9     555\n",
       "dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summing row-wise\n",
    "\n",
    "rev.sum(numeric_only = True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "4089f251",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1606\n",
       "1    2060\n",
       "2     967\n",
       "3    2519\n",
       "4     438\n",
       "5    1935\n",
       "6    1234\n",
       "7    2313\n",
       "8    2623\n",
       "9     555\n",
       "dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rev.sum(numeric_only = True, axis = \"columns\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1cc2e80",
   "metadata": {},
   "source": [
    "### <font color=\"blue\"> Select a column from the DataFrame </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "0c148ca7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "\n",
       "             College     Salary  \n",
       "0              Texas  7730337.0  \n",
       "1          Marquette  6796117.0  \n",
       "2  Boston University        NaN  \n",
       "3      Georgia State  1148640.0  \n",
       "4                NaN  5000000.0  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "3a2ab623",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0      25.0\n",
       "1      25.0\n",
       "2      27.0\n",
       "3      22.0\n",
       "4      29.0\n",
       "       ... \n",
       "453    26.0\n",
       "454    24.0\n",
       "455    26.0\n",
       "456    26.0\n",
       "457     NaN\n",
       "Name: Age, Length: 458, dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this way of accessing  like a attribute doesn't work every time\n",
    "# bcs if the column name contains whitespace char\n",
    "\n",
    "print(type(nba.Age))\n",
    "nba.Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "7461644e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                  Texas\n",
       "1              Marquette\n",
       "2      Boston University\n",
       "3          Georgia State\n",
       "4                    NaN\n",
       "             ...        \n",
       "453               Butler\n",
       "454                  NaN\n",
       "455                  NaN\n",
       "456               Kansas\n",
       "457                  NaN\n",
       "Name: College, Length: 458, dtype: object"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.College"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "d7fbe994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                  Texas\n",
       "1              Marquette\n",
       "2      Boston University\n",
       "3          Georgia State\n",
       "4                    NaN\n",
       "             ...        \n",
       "453               Butler\n",
       "454                  NaN\n",
       "455                  NaN\n",
       "456               Kansas\n",
       "457                  NaN\n",
       "Name: College, Length: 458, dtype: object"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# but this will work even for whitespace char in the column name\n",
    "\n",
    "nba[\"College\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "0ada1a40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Texas', 'Marquette', 'Boston University', 'Georgia State', nan],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"College\"].head().values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "180cc943",
   "metadata": {},
   "source": [
    "####  Selecting two or more col from the dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "d6115b22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "\n",
       "             College     Salary  \n",
       "0              Texas  7730337.0  \n",
       "1          Marquette  6796117.0  \n",
       "2  Boston University        NaN  \n",
       "3      Georgia State  1148640.0  \n",
       "4                NaN  5000000.0  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "d505e549",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>College</th>\n",
       "      <th>Position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>Texas</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>SF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>SG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>SG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Team            College Position\n",
       "0  Boston Celtics              Texas       PG\n",
       "1  Boston Celtics          Marquette       SF\n",
       "2  Boston Celtics  Boston University       SG\n",
       "3  Boston Celtics      Georgia State       SG\n",
       "4  Boston Celtics                NaN       PF"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# result would be dataframe with the order of col name pass, if col name doesn't exist we get KeyError\n",
    "\n",
    "col_to_select = [\"Team\", \"College\", \"Position\"]\n",
    "nba[col_to_select].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "e05c9713",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.0</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.0</td>\n",
       "      <td>SF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.0</td>\n",
       "      <td>SG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.0</td>\n",
       "      <td>SG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29.0</td>\n",
       "      <td>PF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Position\n",
       "0  25.0       PG\n",
       "1  25.0       SF\n",
       "2  27.0       SG\n",
       "3  22.0       SG\n",
       "4  29.0       PF"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can also use double sq bracket format\n",
    "\n",
    "nba[[\"Age\", \"Position\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "5e5336de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>College</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Boston Celtics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Marquette</td>\n",
       "      <td>Boston Celtics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Boston University</td>\n",
       "      <td>Boston Celtics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Georgia State</td>\n",
       "      <td>Boston Celtics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Boston Celtics</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             College            Team\n",
       "0              Texas  Boston Celtics\n",
       "1          Marquette  Boston Celtics\n",
       "2  Boston University  Boston Celtics\n",
       "3      Georgia State  Boston Celtics\n",
       "4                NaN  Boston Celtics"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nba[\"College\", \"Team\"] -> KeyError\n",
    "\n",
    "nba[col_to_select][[\"College\", \"Team\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "914231a7",
   "metadata": {},
   "source": [
    "### <font color=blue> Add a new col to the dataframe </font>\n",
    "\n",
    "* We can add column using the following ways:\n",
    "   1. Add columns at the end of the table.\n",
    "   2. Add columns at a specific index using `insert` method\n",
    "   3. Add columns with the `loc` method.\n",
    "   4. Add columns with the `assign` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "f6846db5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "      <th>MyCol1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "      <td>Fills</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "      <td>Fills</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fills</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "      <td>Fills</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "      <td>Fills</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "\n",
       "             College     Salary MyCol1  \n",
       "0              Texas  7730337.0  Fills  \n",
       "1          Marquette  6796117.0  Fills  \n",
       "2  Boston University        NaN  Fills  \n",
       "3      Georgia State  1148640.0  Fills  \n",
       "4                NaN  5000000.0  Fills  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding col at the end of the datframe, using assignment operator\n",
    "\n",
    "nba[\"MyCol1\"] = \"Fills\"\n",
    "nba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "5cbcfbcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "      <th>MyCol1</th>\n",
       "      <th>MyCol2</th>\n",
       "      <th>MyCol3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "      <td>Fills</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "      <td>Fills</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fills</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "      <td>Fills</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "      <td>Fills</td>\n",
       "      <td>N/A</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "\n",
       "             College     Salary MyCol1 MyCol2 MyCol3  \n",
       "0              Texas  7730337.0  Fills    N/A    N/A  \n",
       "1          Marquette  6796117.0  Fills    N/A    N/A  \n",
       "2  Boston University        NaN  Fills    N/A    N/A  \n",
       "3      Georgia State  1148640.0  Fills    N/A    N/A  \n",
       "4                NaN  5000000.0  Fills    N/A    N/A  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can add multiple cols as well\n",
    "nba[[\"MyCol2\", \"MyCol3\"]] = \"N/A\"\n",
    "nba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "d2f9c975",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "\n",
       "             College     Salary  \n",
       "0              Texas  7730337.0  \n",
       "1          Marquette  6796117.0  \n",
       "2  Boston University        NaN  \n",
       "3      Georgia State  1148640.0  \n",
       "4                NaN  5000000.0  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now lets drop this cols\n",
    "\n",
    "nba.drop([\"MyCol1\", \"MyCol2\", \"MyCol3\"], axis = 1, inplace = True)\n",
    "nba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "3ddbc8ed",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Col_X</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team Col_X  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics   NaN     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics   NaN    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics   NaN    30.0       SG  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics   NaN    28.0       SG  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics   NaN     8.0       PF  29.0   6-10   231.0   \n",
       "\n",
       "             College     Salary  \n",
       "0              Texas  7730337.0  \n",
       "1          Marquette  6796117.0  \n",
       "2  Boston University        NaN  \n",
       "3      Georgia State  1148640.0  \n",
       "4                NaN  5000000.0  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding col to the dataframe at specified loc using insert method\n",
    "\n",
    "nba.insert(loc = 2, column = \"Col_X\", value = \"NaN\")\n",
    "nba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "2426e415",
   "metadata": {},
   "outputs": [],
   "source": [
    "nba.drop(labels = [\"Col_X\"], inplace = True, axis = \"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "fa803567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "\n",
       "             College     Salary  \n",
       "0              Texas  7730337.0  \n",
       "1          Marquette  6796117.0  \n",
       "2  Boston University        NaN  \n",
       "3      Georgia State  1148640.0  \n",
       "4                NaN  5000000.0  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0405e4a1",
   "metadata": {},
   "source": [
    "### <font color=blue> Create a new col from existing col </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "5b171ede",
   "metadata": {},
   "outputs": [],
   "source": [
    "nba[\"Age after a Decade\"] = nba[\"Age\"] + 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "e09a1a99",
   "metadata": {},
   "outputs": [],
   "source": [
    "nba[\"Salary Hike\"] = nba[\"Salary\"].mul(0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "fdf78dfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Age after a Decade</th>\n",
       "      <th>Salary Hike</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>386516.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>339805.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>57432.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>250000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "\n",
       "             College     Salary  Age after a Decade  Salary Hike  \n",
       "0              Texas  7730337.0                35.0    386516.85  \n",
       "1          Marquette  6796117.0                35.0    339805.85  \n",
       "2  Boston University        NaN                37.0          NaN  \n",
       "3      Georgia State  1148640.0                32.0     57432.00  \n",
       "4                NaN  5000000.0                39.0    250000.00  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c27f9594",
   "metadata": {},
   "source": [
    "#### Review of value_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "24cc6033",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SG    102\n",
       "PF    100\n",
       "PG     92\n",
       "SF     85\n",
       "C      78\n",
       "Name: Position, dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"Position\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a77af708",
   "metadata": {},
   "source": [
    "### <font color=blue> Drop Rows with Null values </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "cb637d7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>Name</th>\n",
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       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
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       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Age after a Decade</th>\n",
       "      <th>Salary Hike</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Butler</td>\n",
       "      <td>2433333.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>121666.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Raul Neto</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>25.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-1</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900000.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>45000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>Tibor Pleiss</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>21.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-3</td>\n",
       "      <td>256.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2900000.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>145000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>947276.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>47363.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name       Team  Number Position   Age Height  Weight College  \\\n",
       "453  Shelvin Mack  Utah Jazz     8.0       PG  26.0    6-3   203.0  Butler   \n",
       "454     Raul Neto  Utah Jazz    25.0       PG  24.0    6-1   179.0     NaN   \n",
       "455  Tibor Pleiss  Utah Jazz    21.0        C  26.0    7-3   256.0     NaN   \n",
       "456   Jeff Withey  Utah Jazz    24.0        C  26.0    7-0   231.0  Kansas   \n",
       "457           NaN        NaN     NaN      NaN   NaN    NaN     NaN     NaN   \n",
       "\n",
       "        Salary  Age after a Decade  Salary Hike  \n",
       "453  2433333.0                36.0    121666.65  \n",
       "454   900000.0                34.0     45000.00  \n",
       "455  2900000.0                36.0    145000.00  \n",
       "456   947276.0                36.0     47363.80  \n",
       "457        NaN                 NaN          NaN  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "68958cd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
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       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
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       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>339805.85</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>57432.00</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jordan Mickey</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>55.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>235.0</td>\n",
       "      <td>LSU</td>\n",
       "      <td>1170960.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>58548.00</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kelly Olynyk</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>41.0</td>\n",
       "      <td>C</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>Gonzaga</td>\n",
       "      <td>2165160.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>108258.00</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>Rodney Hood</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>5.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>206.0</td>\n",
       "      <td>Duke</td>\n",
       "      <td>1348440.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>67422.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>Chris Johnson</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>23.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>206.0</td>\n",
       "      <td>Dayton</td>\n",
       "      <td>981348.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49067.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>Trey Lyles</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>41.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>234.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>2239800.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>111990.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Butler</td>\n",
       "      <td>2433333.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>121666.65</td>\n",
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       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>947276.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>47363.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>364 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name            Team  Number Position   Age Height  Weight  \\\n",
       "0    Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1      Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "3      R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "6    Jordan Mickey  Boston Celtics    55.0       PF  21.0    6-8   235.0   \n",
       "7     Kelly Olynyk  Boston Celtics    41.0        C  25.0    7-0   238.0   \n",
       "..             ...             ...     ...      ...   ...    ...     ...   \n",
       "449    Rodney Hood       Utah Jazz     5.0       SG  23.0    6-8   206.0   \n",
       "451  Chris Johnson       Utah Jazz    23.0       SF  26.0    6-6   206.0   \n",
       "452     Trey Lyles       Utah Jazz    41.0       PF  20.0   6-10   234.0   \n",
       "453   Shelvin Mack       Utah Jazz     8.0       PG  26.0    6-3   203.0   \n",
       "456    Jeff Withey       Utah Jazz    24.0        C  26.0    7-0   231.0   \n",
       "\n",
       "           College     Salary  Age after a Decade  Salary Hike  \n",
       "0            Texas  7730337.0                35.0    386516.85  \n",
       "1        Marquette  6796117.0                35.0    339805.85  \n",
       "3    Georgia State  1148640.0                32.0     57432.00  \n",
       "6              LSU  1170960.0                31.0     58548.00  \n",
       "7          Gonzaga  2165160.0                35.0    108258.00  \n",
       "..             ...        ...                 ...          ...  \n",
       "449           Duke  1348440.0                33.0     67422.00  \n",
       "451         Dayton   981348.0                36.0     49067.40  \n",
       "452       Kentucky  2239800.0                30.0    111990.00  \n",
       "453         Butler  2433333.0                36.0    121666.65  \n",
       "456         Kansas   947276.0                36.0     47363.80  \n",
       "\n",
       "[364 rows x 11 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
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   "source": [
    "# to remove any rows which have NaN values\n",
    "\n",
    "nba.dropna()\n",
    "# nba.dropna(how=\"any\")    # same as above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "ccd6735f",
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   "outputs": [
    {
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       "      <td>32.0</td>\n",
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       "      <td>Boston Celtics</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>Trey Lyles</td>\n",
       "      <td>Utah Jazz</td>\n",
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       "      <td>PF</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>234.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>2239800.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>111990.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>8.0</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Butler</td>\n",
       "      <td>2433333.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>121666.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Raul Neto</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>25.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-1</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900000.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>45000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>Tibor Pleiss</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>21.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-3</td>\n",
       "      <td>256.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2900000.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>145000.00</td>\n",
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       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
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       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>947276.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>47363.80</td>\n",
       "    </tr>\n",
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       "<p>457 rows × 11 columns</p>\n",
       "</div>"
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      "text/plain": [
       "              Name            Team  Number Position   Age Height  Weight  \\\n",
       "0    Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1      Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2     John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3      R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4    Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "..             ...             ...     ...      ...   ...    ...     ...   \n",
       "452     Trey Lyles       Utah Jazz    41.0       PF  20.0   6-10   234.0   \n",
       "453   Shelvin Mack       Utah Jazz     8.0       PG  26.0    6-3   203.0   \n",
       "454      Raul Neto       Utah Jazz    25.0       PG  24.0    6-1   179.0   \n",
       "455   Tibor Pleiss       Utah Jazz    21.0        C  26.0    7-3   256.0   \n",
       "456    Jeff Withey       Utah Jazz    24.0        C  26.0    7-0   231.0   \n",
       "\n",
       "               College     Salary  Age after a Decade  Salary Hike  \n",
       "0                Texas  7730337.0                35.0    386516.85  \n",
       "1            Marquette  6796117.0                35.0    339805.85  \n",
       "2    Boston University        NaN                37.0          NaN  \n",
       "3        Georgia State  1148640.0                32.0     57432.00  \n",
       "4                  NaN  5000000.0                39.0    250000.00  \n",
       "..                 ...        ...                 ...          ...  \n",
       "452           Kentucky  2239800.0                30.0    111990.00  \n",
       "453             Butler  2433333.0                36.0    121666.65  \n",
       "454                NaN   900000.0                34.0     45000.00  \n",
       "455                NaN  2900000.0                36.0    145000.00  \n",
       "456             Kansas   947276.0                36.0     47363.80  \n",
       "\n",
       "[457 rows x 11 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to remove rows which have all col val as NaN\n",
    "\n",
    "nba.dropna(how = \"all\")   # last row is removed only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "da8188c9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Age after a Decade</th>\n",
       "      <th>Salary Hike</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>386516.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>339805.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>57432.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jordan Mickey</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>55.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>235.0</td>\n",
       "      <td>LSU</td>\n",
       "      <td>1170960.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>58548.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kelly Olynyk</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>41.0</td>\n",
       "      <td>C</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>Gonzaga</td>\n",
       "      <td>2165160.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>108258.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>Rodney Hood</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>5.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>206.0</td>\n",
       "      <td>Duke</td>\n",
       "      <td>1348440.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>67422.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>Chris Johnson</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>23.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>206.0</td>\n",
       "      <td>Dayton</td>\n",
       "      <td>981348.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49067.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>Trey Lyles</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>41.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>234.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>2239800.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>111990.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Butler</td>\n",
       "      <td>2433333.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>121666.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>947276.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>47363.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>364 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name            Team  Number Position   Age Height  Weight  \\\n",
       "0    Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1      Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "3      R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "6    Jordan Mickey  Boston Celtics    55.0       PF  21.0    6-8   235.0   \n",
       "7     Kelly Olynyk  Boston Celtics    41.0        C  25.0    7-0   238.0   \n",
       "..             ...             ...     ...      ...   ...    ...     ...   \n",
       "449    Rodney Hood       Utah Jazz     5.0       SG  23.0    6-8   206.0   \n",
       "451  Chris Johnson       Utah Jazz    23.0       SF  26.0    6-6   206.0   \n",
       "452     Trey Lyles       Utah Jazz    41.0       PF  20.0   6-10   234.0   \n",
       "453   Shelvin Mack       Utah Jazz     8.0       PG  26.0    6-3   203.0   \n",
       "456    Jeff Withey       Utah Jazz    24.0        C  26.0    7-0   231.0   \n",
       "\n",
       "           College     Salary  Age after a Decade  Salary Hike  \n",
       "0            Texas  7730337.0                35.0    386516.85  \n",
       "1        Marquette  6796117.0                35.0    339805.85  \n",
       "3    Georgia State  1148640.0                32.0     57432.00  \n",
       "6              LSU  1170960.0                31.0     58548.00  \n",
       "7          Gonzaga  2165160.0                35.0    108258.00  \n",
       "..             ...        ...                 ...          ...  \n",
       "449           Duke  1348440.0                33.0     67422.00  \n",
       "451         Dayton   981348.0                36.0     49067.40  \n",
       "452       Kentucky  2239800.0                30.0    111990.00  \n",
       "453         Butler  2433333.0                36.0    121666.65  \n",
       "456         Kansas   947276.0                36.0     47363.80  \n",
       "\n",
       "[364 rows x 11 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to reomve rows which have certain col value as NaN\n",
    "\n",
    "nba.dropna(subset = [\"College\", \"Salary Hike\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "7ef3f59c",
   "metadata": {},
   "outputs": [],
   "source": [
    "nba.drop([\"Age after a Decade\", \"Salary Hike\"], inplace = True, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fbb5f17",
   "metadata": {},
   "source": [
    "### <font color=blue>Fill in Missing DataFrame Values with the fillna Method</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "0c9c32e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
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       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Butler</td>\n",
       "      <td>2433333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Raul Neto</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>25.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-1</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>Tibor Pleiss</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>21.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-3</td>\n",
       "      <td>256.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name       Team  Number Position   Age Height  Weight College  \\\n",
       "453  Shelvin Mack  Utah Jazz     8.0       PG  26.0    6-3   203.0  Butler   \n",
       "454     Raul Neto  Utah Jazz    25.0       PG  24.0    6-1   179.0     NaN   \n",
       "455  Tibor Pleiss  Utah Jazz    21.0        C  26.0    7-3   256.0     NaN   \n",
       "456   Jeff Withey  Utah Jazz    24.0        C  26.0    7-0   231.0  Kansas   \n",
       "457           NaN        NaN     NaN      NaN   NaN    NaN     NaN     NaN   \n",
       "\n",
       "        Salary  \n",
       "453  2433333.0  \n",
       "454   900000.0  \n",
       "455  2900000.0  \n",
       "456   947276.0  \n",
       "457        NaN  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "a97557f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Name</th>\n",
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       "      <th>Position</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5000000.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Butler</td>\n",
       "      <td>2433333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Raul Neto</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>25.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-1</td>\n",
       "      <td>179.0</td>\n",
       "      <td>0</td>\n",
       "      <td>900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>Tibor Pleiss</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>21.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-3</td>\n",
       "      <td>256.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>458 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name            Team  Number Position   Age Height  Weight  \\\n",
       "0    Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1      Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2     John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3      R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4    Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "..             ...             ...     ...      ...   ...    ...     ...   \n",
       "453   Shelvin Mack       Utah Jazz     8.0       PG  26.0    6-3   203.0   \n",
       "454      Raul Neto       Utah Jazz    25.0       PG  24.0    6-1   179.0   \n",
       "455   Tibor Pleiss       Utah Jazz    21.0        C  26.0    7-3   256.0   \n",
       "456    Jeff Withey       Utah Jazz    24.0        C  26.0    7-0   231.0   \n",
       "457              0               0     0.0        0   0.0      0     0.0   \n",
       "\n",
       "               College     Salary  \n",
       "0                Texas  7730337.0  \n",
       "1            Marquette  6796117.0  \n",
       "2    Boston University        0.0  \n",
       "3        Georgia State  1148640.0  \n",
       "4                    0  5000000.0  \n",
       "..                 ...        ...  \n",
       "453             Butler  2433333.0  \n",
       "454                  0   900000.0  \n",
       "455                  0  2900000.0  \n",
       "456             Kansas   947276.0  \n",
       "457                  0        0.0  \n",
       "\n",
       "[458 rows x 9 columns]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill all the NaN values to 0, and this will gives new df\n",
    "\n",
    "nba.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "a6ea872e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Butler</td>\n",
       "      <td>2433333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Raul Neto</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>25.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-1</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>Tibor Pleiss</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>21.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-3</td>\n",
       "      <td>256.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name       Team  Number Position   Age Height  Weight College  \\\n",
       "453  Shelvin Mack  Utah Jazz     8.0       PG  26.0    6-3   203.0  Butler   \n",
       "454     Raul Neto  Utah Jazz    25.0       PG  24.0    6-1   179.0     NaN   \n",
       "455  Tibor Pleiss  Utah Jazz    21.0        C  26.0    7-3   256.0     NaN   \n",
       "456   Jeff Withey  Utah Jazz    24.0        C  26.0    7-0   231.0  Kansas   \n",
       "457           NaN        NaN     NaN      NaN   NaN    NaN     NaN     NaN   \n",
       "\n",
       "        Salary  \n",
       "453  2433333.0  \n",
       "454   900000.0  \n",
       "455  2900000.0  \n",
       "456   947276.0  \n",
       "457        NaN  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "00645cd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                  Texas\n",
       "1              Marquette\n",
       "2      Boston University\n",
       "3          Georgia State\n",
       "4                Unknown\n",
       "             ...        \n",
       "453               Butler\n",
       "454              Unknown\n",
       "455              Unknown\n",
       "456               Kansas\n",
       "457              Unknown\n",
       "Name: College, Length: 458, dtype: object"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filling NaN of College to Unknown\n",
    "\n",
    "nba[\"College\"].fillna(\"Unknown\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "33e08e1f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>454</th>\n",
       "      <td>Raul Neto</td>\n",
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       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
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       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name       Team  Number Position   Age Height  Weight College  \\\n",
       "453  Shelvin Mack  Utah Jazz     8.0       PG  26.0    6-3   203.0  Butler   \n",
       "454     Raul Neto  Utah Jazz    25.0       PG  24.0    6-1   179.0     NaN   \n",
       "455  Tibor Pleiss  Utah Jazz    21.0        C  26.0    7-3   256.0     NaN   \n",
       "456   Jeff Withey  Utah Jazz    24.0        C  26.0    7-0   231.0  Kansas   \n",
       "457           NaN        NaN     NaN      NaN   NaN    NaN     NaN     NaN   \n",
       "\n",
       "        Salary  \n",
       "453  2433333.0  \n",
       "454   900000.0  \n",
       "455  2900000.0  \n",
       "456   947276.0  \n",
       "457        NaN  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "222b36e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Butler</td>\n",
       "      <td>2433333.0</td>\n",
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       "      <th>454</th>\n",
       "      <td>Raul Neto</td>\n",
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       "      <th>455</th>\n",
       "      <td>Tibor Pleiss</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>21.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-3</td>\n",
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       "      <td>Unknown</td>\n",
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       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name       Team  Number Position   Age Height  Weight  College  \\\n",
       "453  Shelvin Mack  Utah Jazz     8.0       PG  26.0    6-3   203.0   Butler   \n",
       "454     Raul Neto  Utah Jazz    25.0       PG  24.0    6-1   179.0  Unknown   \n",
       "455  Tibor Pleiss  Utah Jazz    21.0        C  26.0    7-3   256.0  Unknown   \n",
       "456   Jeff Withey  Utah Jazz    24.0        C  26.0    7-0   231.0   Kansas   \n",
       "457           NaN        NaN     NaN      NaN   NaN    NaN     NaN  Unknown   \n",
       "\n",
       "        Salary  \n",
       "453  2433333.0  \n",
       "454   900000.0  \n",
       "455  2900000.0  \n",
       "456   947276.0  \n",
       "457        NaN  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mutating the df as well either using inplace = True or using assignment operator\n",
    "\n",
    "nba[\"College\"].fillna(\"Unknown\", inplace = True)\n",
    "nba.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "826a2d3d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Butler</td>\n",
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       "      <th>454</th>\n",
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       "      <td>Utah Jazz</td>\n",
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       "      <th>455</th>\n",
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       "      <td>Utah Jazz</td>\n",
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       "      <td>Unknown</td>\n",
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       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>Utah Jazz</td>\n",
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       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name       Team  Number Position   Age Height  Weight  College  \\\n",
       "453  Shelvin Mack  Utah Jazz     8.0       PG  26.0    6-3   203.0   Butler   \n",
       "454     Raul Neto  Utah Jazz    25.0       PG  24.0    6-1   179.0  Unknown   \n",
       "455  Tibor Pleiss  Utah Jazz    21.0        C  26.0    7-3   256.0  Unknown   \n",
       "456   Jeff Withey  Utah Jazz    24.0        C  26.0    7-0   231.0   Kansas   \n",
       "457           NaN        NaN     NaN      NaN   NaN    NaN     NaN  Unknown   \n",
       "\n",
       "        Salary  \n",
       "453  2433333.0  \n",
       "454   900000.0  \n",
       "455  2900000.0  \n",
       "456   947276.0  \n",
       "457        0.0  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"Salary\"] = nba[\"Salary\"].fillna(0)\n",
    "nba.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ad8d74c",
   "metadata": {},
   "source": [
    "### <font color=blue> astype method </font>\n",
    "\n",
    "* `astype` method cannot convert NaN values to the specified datatype. So, it's good to first deal with NaN values either by `fillna` or `dropna`\n",
    "* If there is a NaN value in a numerical column (either it's int), then that whole column wil be converted as a *float* data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "1d4c8bf6",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
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       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>Trey Lyles</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>41.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>234.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>2239800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Shelvin Mack</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Butler</td>\n",
       "      <td>2433333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Raul Neto</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>25.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-1</td>\n",
       "      <td>179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>Tibor Pleiss</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>21.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-3</td>\n",
       "      <td>256.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jeff Withey</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>24.0</td>\n",
       "      <td>C</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7-0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>947276.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>457 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name            Team  Number Position   Age Height  Weight  \\\n",
       "0    Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1      Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2     John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3      R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4    Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "..             ...             ...     ...      ...   ...    ...     ...   \n",
       "452     Trey Lyles       Utah Jazz    41.0       PF  20.0   6-10   234.0   \n",
       "453   Shelvin Mack       Utah Jazz     8.0       PG  26.0    6-3   203.0   \n",
       "454      Raul Neto       Utah Jazz    25.0       PG  24.0    6-1   179.0   \n",
       "455   Tibor Pleiss       Utah Jazz    21.0        C  26.0    7-3   256.0   \n",
       "456    Jeff Withey       Utah Jazz    24.0        C  26.0    7-0   231.0   \n",
       "\n",
       "               College     Salary  \n",
       "0                Texas  7730337.0  \n",
       "1            Marquette  6796117.0  \n",
       "2    Boston University        NaN  \n",
       "3        Georgia State  1148640.0  \n",
       "4                  NaN  5000000.0  \n",
       "..                 ...        ...  \n",
       "452           Kentucky  2239800.0  \n",
       "453             Butler  2433333.0  \n",
       "454                NaN   900000.0  \n",
       "455                NaN  2900000.0  \n",
       "456             Kansas   947276.0  \n",
       "\n",
       "[457 rows x 9 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba = pd.read_csv(\"./datasets/nba.csv\").dropna(how = \"all\")\n",
    "nba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "493a0ea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"Age\"].hasnans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "7c780403",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      25\n",
       "1      25\n",
       "2      27\n",
       "3      22\n",
       "4      29\n",
       "       ..\n",
       "452    20\n",
       "453    26\n",
       "454    24\n",
       "455    26\n",
       "456    26\n",
       "Name: Age, Length: 457, dtype: int16"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"Age\"].astype(\"int16\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "d6695688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# above process is not reflected to original df\n",
    "\n",
    "nba[\"Age\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "51336625",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to change the original df use assignment operator\n",
    "\n",
    "nba[\"Age\"] = nba[\"Age\"].astype(\"int16\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "ecf91d08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int16')"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"Age\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "269996d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"Salary\"].hasnans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "ed90c738",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now to convert Salary column dtype to string\n",
    "# first fill the missing val\n",
    "\n",
    "nba[\"Salary\"].fillna(value = 0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "46b9d57f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position  Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG   25    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF   25    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG   27    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG   22    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF   29   6-10   231.0   \n",
       "\n",
       "             College     Salary  \n",
       "0              Texas  7730337.0  \n",
       "1          Marquette  6796117.0  \n",
       "2  Boston University        0.0  \n",
       "3      Georgia State  1148640.0  \n",
       "4                NaN  5000000.0  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "f9de3f87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position  Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG   25    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF   25    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG   27    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG   22    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF   29   6-10   231.0   \n",
       "\n",
       "             College   Salary  \n",
       "0              Texas  7730337  \n",
       "1          Marquette  6796117  \n",
       "2  Boston University        0  \n",
       "3      Georgia State  1148640  \n",
       "4                NaN  5000000  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# float -> int -> str\n",
    "\n",
    "nba[\"Salary\"] = nba[\"Salary\"].astype(\"int\").astype(\"str\")\n",
    "nba.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dc59447",
   "metadata": {},
   "source": [
    "####  category datatype\n",
    "\n",
    "* Why use categorical datatypes in pandas?\n",
    "    1. Memory usage — for string columns where there are many repeated values, categories can drastically reduce the amount of memory required to store the data in memory\n",
    "    2. Runtime performance — there are optimizations in place which can improve execution speed for certain operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "d4309c8d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 457 entries, 0 to 456\n",
      "Data columns (total 9 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Name      457 non-null    object \n",
      " 1   Team      457 non-null    object \n",
      " 2   Number    457 non-null    float64\n",
      " 3   Position  457 non-null    object \n",
      " 4   Age       457 non-null    int16  \n",
      " 5   Height    457 non-null    object \n",
      " 6   Weight    457 non-null    float64\n",
      " 7   College   373 non-null    object \n",
      " 8   Salary    457 non-null    object \n",
      "dtypes: float64(2), int16(1), object(6)\n",
      "memory usage: 33.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# mind the memory-usage\n",
    "\n",
    "nba.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "5ce6393f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30541"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# it's taking around 30 KB, before category\n",
    "\n",
    "nba[\"Position\"].memory_usage(deep = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "6695e453",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# str type\n",
    "\n",
    "nba[\"Position\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "fddebd1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SG    102\n",
       "PF    100\n",
       "PG     92\n",
       "SF     85\n",
       "C      78\n",
       "Name: Position, dtype: int64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"Position\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "7f79c5b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"Position\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "2f195f13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      PG\n",
       "1      SF\n",
       "2      SG\n",
       "3      SG\n",
       "4      PF\n",
       "       ..\n",
       "452    PF\n",
       "453    PG\n",
       "454    PG\n",
       "455     C\n",
       "456     C\n",
       "Name: Position, Length: 457, dtype: category\n",
       "Categories (5, object): ['C', 'PF', 'PG', 'SF', 'SG']"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"Position\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "f4009dd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# as there are only 5 unique values we can categorize them rather than re-creating str again and again\n",
    "\n",
    "nba[\"Position\"] = nba[\"Position\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "fefc51c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=['C', 'PF', 'PG', 'SF', 'SG'], ordered=False)"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"Position\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "b721c419",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 457 entries, 0 to 456\n",
      "Data columns (total 9 columns):\n",
      " #   Column    Non-Null Count  Dtype   \n",
      "---  ------    --------------  -----   \n",
      " 0   Name      457 non-null    object  \n",
      " 1   Team      457 non-null    object  \n",
      " 2   Number    457 non-null    float64 \n",
      " 3   Position  457 non-null    category\n",
      " 4   Age       457 non-null    int16   \n",
      " 5   Height    457 non-null    object  \n",
      " 6   Weight    457 non-null    float64 \n",
      " 7   College   373 non-null    object  \n",
      " 8   Salary    457 non-null    object  \n",
      "dtypes: category(1), float64(2), int16(1), object(5)\n",
      "memory usage: 30.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# previosly memory usage is 33KB and now it's 30KB\n",
    "\n",
    "nba.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "8157d519",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4579"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now it's taking 4KB, category dtype is useful for repeated data\n",
    "\n",
    "nba[\"Position\"].memory_usage(deep = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70516cd2",
   "metadata": {},
   "source": [
    "### <font color=blue> Sort a DataFrame with the sort_values method </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "c1e25839",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "\n",
       "             College     Salary  \n",
       "0              Texas  7730337.0  \n",
       "1          Marquette  6796117.0  \n",
       "2  Boston University        NaN  \n",
       "3      Georgia State  1148640.0  \n",
       "4                NaN  5000000.0  "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba = pd.read_csv(\"./datasets/nba.csv\")\n",
    "nba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "b2a2a5c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>Zaza Pachulia</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>27.0</td>\n",
       "      <td>C</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6-11</td>\n",
       "      <td>275.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>Zach Randolph</td>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>50.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>34.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>260.0</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>9638555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Zach LaVine</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>189.0</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>2148360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>Xavier Munford</td>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>14.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>Wilson Chandler</td>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>21.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>225.0</td>\n",
       "      <td>DePaul</td>\n",
       "      <td>10449438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>Adreian Payne</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>33.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>237.0</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>1938840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>Aaron Harrison</td>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>210.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>525093.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>Aaron Gordon</td>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>4171680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Aaron Brooks</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6-0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>2250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>458 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name                    Team  Number Position   Age Height  \\\n",
       "237    Zaza Pachulia        Dallas Mavericks    27.0        C  32.0   6-11   \n",
       "271    Zach Randolph       Memphis Grizzlies    50.0       PF  34.0    6-9   \n",
       "402      Zach LaVine  Minnesota Timberwolves     8.0       PG  21.0    6-5   \n",
       "270   Xavier Munford       Memphis Grizzlies    14.0       PG  24.0    6-3   \n",
       "386  Wilson Chandler          Denver Nuggets    21.0       SF  29.0    6-8   \n",
       "..               ...                     ...     ...      ...   ...    ...   \n",
       "404    Adreian Payne  Minnesota Timberwolves    33.0       PF  25.0   6-10   \n",
       "328   Aaron Harrison       Charlotte Hornets     9.0       SG  21.0    6-6   \n",
       "356     Aaron Gordon           Orlando Magic     0.0       PF  20.0    6-9   \n",
       "152     Aaron Brooks           Chicago Bulls     0.0       PG  31.0    6-0   \n",
       "457              NaN                     NaN     NaN      NaN   NaN    NaN   \n",
       "\n",
       "     Weight         College      Salary  \n",
       "237   275.0             NaN   5200000.0  \n",
       "271   260.0  Michigan State   9638555.0  \n",
       "402   189.0            UCLA   2148360.0  \n",
       "270   180.0    Rhode Island         NaN  \n",
       "386   225.0          DePaul  10449438.0  \n",
       "..      ...             ...         ...  \n",
       "404   237.0  Michigan State   1938840.0  \n",
       "328   210.0        Kentucky    525093.0  \n",
       "356   220.0         Arizona   4171680.0  \n",
       "152   161.0          Oregon   2250000.0  \n",
       "457     NaN             NaN         NaN  \n",
       "\n",
       "[458 rows x 9 columns]"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sorting wrt name in descending order\n",
    "\n",
    "nba.sort_values(by = \"Name\", ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "6dc42a76",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>152</th>\n",
       "      <td>Aaron Brooks</td>\n",
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       "      <td>161.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>2250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
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       "      <td>Orlando Magic</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>20.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>4171680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>Aaron Harrison</td>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>210.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>525093.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>Adreian Payne</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>33.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>237.0</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>1938840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Al Horford</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>15.0</td>\n",
       "      <td>C</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>245.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>Xavier Munford</td>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>14.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Zach LaVine</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>189.0</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>2148360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>Zach Randolph</td>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>50.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>34.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>260.0</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>9638555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>Zaza Pachulia</td>\n",
       "      <td>Dallas Mavericks</td>\n",
       "      <td>27.0</td>\n",
       "      <td>C</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6-11</td>\n",
       "      <td>275.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>458 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name                    Team  Number Position   Age Height  \\\n",
       "152    Aaron Brooks           Chicago Bulls     0.0       PG  31.0    6-0   \n",
       "356    Aaron Gordon           Orlando Magic     0.0       PF  20.0    6-9   \n",
       "328  Aaron Harrison       Charlotte Hornets     9.0       SG  21.0    6-6   \n",
       "404   Adreian Payne  Minnesota Timberwolves    33.0       PF  25.0   6-10   \n",
       "312      Al Horford           Atlanta Hawks    15.0        C  30.0   6-10   \n",
       "..              ...                     ...     ...      ...   ...    ...   \n",
       "270  Xavier Munford       Memphis Grizzlies    14.0       PG  24.0    6-3   \n",
       "402     Zach LaVine  Minnesota Timberwolves     8.0       PG  21.0    6-5   \n",
       "271   Zach Randolph       Memphis Grizzlies    50.0       PF  34.0    6-9   \n",
       "237   Zaza Pachulia        Dallas Mavericks    27.0        C  32.0   6-11   \n",
       "457             NaN                     NaN     NaN      NaN   NaN    NaN   \n",
       "\n",
       "     Weight         College      Salary  \n",
       "152   161.0          Oregon   2250000.0  \n",
       "356   220.0         Arizona   4171680.0  \n",
       "328   210.0        Kentucky    525093.0  \n",
       "404   237.0  Michigan State   1938840.0  \n",
       "312   245.0         Florida  12000000.0  \n",
       "..      ...             ...         ...  \n",
       "270   180.0    Rhode Island         NaN  \n",
       "402   189.0            UCLA   2148360.0  \n",
       "271   260.0  Michigan State   9638555.0  \n",
       "237   275.0             NaN   5200000.0  \n",
       "457     NaN             NaN         NaN  \n",
       "\n",
       "[458 rows x 9 columns]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sorting wrt Name, Team with Name as asc and Team as desc\n",
    "\n",
    "nba.sort_values(by = [\"Name\", \"Team\"], ascending = [True, False])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c16437",
   "metadata": {},
   "source": [
    "### <font color=blue> sort_index method to get the original df back </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "2fe8c3fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
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       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "\n",
       "             College     Salary  \n",
       "0              Texas  7730337.0  \n",
       "1          Marquette  6796117.0  \n",
       "2  Boston University        NaN  \n",
       "3      Georgia State  1148640.0  \n",
       "4                NaN  5000000.0  "
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "62e9a3f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Number</th>\n",
       "      <th>Position</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Aaron Brooks</td>\n",
       "      <td>Chicago Bulls</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6-0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>2250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>Aaron Gordon</td>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6-9</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>4171680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>Aaron Harrison</td>\n",
       "      <td>Charlotte Hornets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>210.0</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>525093.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>Adreian Payne</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>33.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>237.0</td>\n",
       "      <td>Michigan State</td>\n",
       "      <td>1938840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Al Horford</td>\n",
       "      <td>Atlanta Hawks</td>\n",
       "      <td>15.0</td>\n",
       "      <td>C</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>245.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>12000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name                    Team  Number Position   Age Height  \\\n",
       "152    Aaron Brooks           Chicago Bulls     0.0       PG  31.0    6-0   \n",
       "356    Aaron Gordon           Orlando Magic     0.0       PF  20.0    6-9   \n",
       "328  Aaron Harrison       Charlotte Hornets     9.0       SG  21.0    6-6   \n",
       "404   Adreian Payne  Minnesota Timberwolves    33.0       PF  25.0   6-10   \n",
       "312      Al Horford           Atlanta Hawks    15.0        C  30.0   6-10   \n",
       "\n",
       "     Weight         College      Salary  \n",
       "152   161.0          Oregon   2250000.0  \n",
       "356   220.0         Arizona   4171680.0  \n",
       "328   210.0        Kentucky    525093.0  \n",
       "404   237.0  Michigan State   1938840.0  \n",
       "312   245.0         Florida  12000000.0  "
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now let's do a inplace sort and try to get the original df back\n",
    "\n",
    "nba = nba.sort_values(by = [\"Name\", \"Team\"])\n",
    "nba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "ef3a42e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "\n",
       "             College     Salary  \n",
       "0              Texas  7730337.0  \n",
       "1          Marquette  6796117.0  \n",
       "2  Boston University        NaN  \n",
       "3      Georgia State  1148640.0  \n",
       "4                NaN  5000000.0  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can use sort_index method to get the original df back after inplace sort\n",
    "\n",
    "nba = nba.sort_index()\n",
    "nba.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27d435df",
   "metadata": {},
   "source": [
    "### <font color=blue> Rank Series Values with the rank Method </font>\n",
    "\n",
    "* Suppose we want to Rank the Salary column, highest salary as 1 and so on.\n",
    "* Let's first replace NaN values with 0\n",
    "* And convert Salary.dtype to int\n",
    "* Now we assign rank to the salary values using `rank` method\n",
    "* Cross-verify by sorting df wrt to Salary col in descending order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "5b9ba750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"Salary\"].hasnans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "6aff7c8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"Salary\"].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "2b4054c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7730337.0</td>\n",
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       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "\n",
       "             College     Salary  \n",
       "0              Texas  7730337.0  \n",
       "1          Marquette  6796117.0  \n",
       "2  Boston University        0.0  "
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"Salary\"] = nba[\"Salary\"].fillna(value = 0)\n",
    "nba.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "8777aafe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"Salary\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "5551eb82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"Salary\"] = nba[\"Salary\"].astype(\"int\")\n",
    "nba.Salary.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "1a40bc0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
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       "      <td>Marquette</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "\n",
       "             College   Salary  \n",
       "0              Texas  7730337  \n",
       "1          Marquette  6796117  \n",
       "2  Boston University        0  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "81b53541",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>Height</th>\n",
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       "      <th>College</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Salary Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avery Bradley</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>7730337</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jae Crowder</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>99.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>6796117</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>0</td>\n",
       "      <td>452.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R.J. Hunter</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>28.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>185.0</td>\n",
       "      <td>Georgia State</td>\n",
       "      <td>1148640</td>\n",
       "      <td>322.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonas Jerebko</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>8.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>231.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000000</td>\n",
       "      <td>147.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name            Team  Number Position   Age Height  Weight  \\\n",
       "0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0   \n",
       "1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   235.0   \n",
       "2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   205.0   \n",
       "3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   185.0   \n",
       "4  Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0   \n",
       "\n",
       "             College   Salary  Salary Rank  \n",
       "0              Texas  7730337         97.0  \n",
       "1          Marquette  6796117        110.0  \n",
       "2  Boston University        0        452.5  \n",
       "3      Georgia State  1148640        322.0  \n",
       "4                NaN  5000000        147.0  "
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba[\"Salary Rank\"] = nba[\"Salary\"].rank(ascending = False)\n",
    "nba.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "4fa361b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Salary Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Kobe Bryant</td>\n",
       "      <td>Los Angeles Lakers</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>37.0</td>\n",
       "      <td>6-6</td>\n",
       "      <td>212.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25000000</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>LeBron James</td>\n",
       "      <td>Cleveland Cavaliers</td>\n",
       "      <td>23.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>250.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22970500</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Carmelo Anthony</td>\n",
       "      <td>New York Knicks</td>\n",
       "      <td>7.0</td>\n",
       "      <td>SF</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6-8</td>\n",
       "      <td>240.0</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>22875000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>Dwight Howard</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>12.0</td>\n",
       "      <td>C</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6-11</td>\n",
       "      <td>265.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22359364</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Chris Bosh</td>\n",
       "      <td>Miami Heat</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6-11</td>\n",
       "      <td>235.0</td>\n",
       "      <td>Georgia Tech</td>\n",
       "      <td>22192730</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>Ray McCallum</td>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>5.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6-3</td>\n",
       "      <td>190.0</td>\n",
       "      <td>Detroit</td>\n",
       "      <td>0</td>\n",
       "      <td>452.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>Greg Smith</td>\n",
       "      <td>Minnesota Timberwolves</td>\n",
       "      <td>4.0</td>\n",
       "      <td>PF</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6-10</td>\n",
       "      <td>250.0</td>\n",
       "      <td>Fresno State</td>\n",
       "      <td>0</td>\n",
       "      <td>452.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Holland</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SG</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6-5</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Boston University</td>\n",
       "      <td>0</td>\n",
       "      <td>452.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>Jordan Farmar</td>\n",
       "      <td>Memphis Grizzlies</td>\n",
       "      <td>4.0</td>\n",
       "      <td>PG</td>\n",
       "      <td>29.0</td>\n",
       "      <td>6-2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>UCLA</td>\n",
       "      <td>0</td>\n",
       "      <td>452.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>452.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>458 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name                    Team  Number Position   Age Height  \\\n",
       "109      Kobe Bryant      Los Angeles Lakers    24.0       SF  37.0    6-6   \n",
       "169     LeBron James     Cleveland Cavaliers    23.0       SF  31.0    6-8   \n",
       "33   Carmelo Anthony         New York Knicks     7.0       SF  32.0    6-8   \n",
       "251    Dwight Howard         Houston Rockets    12.0        C  30.0   6-11   \n",
       "339       Chris Bosh              Miami Heat     1.0       PF  32.0   6-11   \n",
       "..               ...                     ...     ...      ...   ...    ...   \n",
       "269     Ray McCallum       Memphis Grizzlies     5.0       PG  24.0    6-3   \n",
       "409       Greg Smith  Minnesota Timberwolves     4.0       PF  25.0   6-10   \n",
       "2       John Holland          Boston Celtics    30.0       SG  27.0    6-5   \n",
       "264    Jordan Farmar       Memphis Grizzlies     4.0       PG  29.0    6-2   \n",
       "457              NaN                     NaN     NaN      NaN   NaN    NaN   \n",
       "\n",
       "     Weight            College    Salary  Salary Rank  \n",
       "109   212.0                NaN  25000000          1.0  \n",
       "169   250.0                NaN  22970500          2.0  \n",
       "33    240.0           Syracuse  22875000          3.0  \n",
       "251   265.0                NaN  22359364          4.0  \n",
       "339   235.0       Georgia Tech  22192730          5.0  \n",
       "..      ...                ...       ...          ...  \n",
       "269   190.0            Detroit         0        452.5  \n",
       "409   250.0       Fresno State         0        452.5  \n",
       "2     205.0  Boston University         0        452.5  \n",
       "264   180.0               UCLA         0        452.5  \n",
       "457     NaN                NaN         0        452.5  \n",
       "\n",
       "[458 rows x 10 columns]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nba.sort_values(by = \"Salary\", ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "874c2356",
   "metadata": {},
   "source": [
    "## <font color=fe7401> Filtering Data </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26d21883",
   "metadata": {},
   "source": [
    "### <font color=blue> Memory Optimization </font>\n",
    "\n",
    "* Categorized the data wherever required.\n",
    "* Convert the string dated columns to datetime object.\n",
    "* Convert the string True/False column to bool type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "fc3da404",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
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       "      <th>0</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>8/6/1993</td>\n",
       "      <td>12:42 PM</td>\n",
       "      <td>97308</td>\n",
       "      <td>6.945</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>3/31/1996</td>\n",
       "      <td>6:53 AM</td>\n",
       "      <td>61933</td>\n",
       "      <td>4.170</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>4/23/1993</td>\n",
       "      <td>11:17 AM</td>\n",
       "      <td>130590</td>\n",
       "      <td>11.858</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>3/4/2005</td>\n",
       "      <td>1:00 PM</td>\n",
       "      <td>138705</td>\n",
       "      <td>9.340</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>1/24/1998</td>\n",
       "      <td>4:47 PM</td>\n",
       "      <td>101004</td>\n",
       "      <td>1.389</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First Name  Gender Start Date Last Login Time  Salary  Bonus %  \\\n",
       "0    Douglas    Male   8/6/1993        12:42 PM   97308    6.945   \n",
       "1     Thomas    Male  3/31/1996         6:53 AM   61933    4.170   \n",
       "2      Maria  Female  4/23/1993        11:17 AM  130590   11.858   \n",
       "3      Jerry    Male   3/4/2005         1:00 PM  138705    9.340   \n",
       "4      Larry    Male  1/24/1998         4:47 PM  101004    1.389   \n",
       "\n",
       "  Senior Management             Team  \n",
       "0              True        Marketing  \n",
       "1              True              NaN  \n",
       "2             False          Finance  \n",
       "3              True          Finance  \n",
       "4              True  Client Services  "
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./datasets/employees.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "4e5cdb66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 8 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   First Name         933 non-null    object \n",
      " 1   Gender             855 non-null    object \n",
      " 2   Start Date         1000 non-null   object \n",
      " 3   Last Login Time    1000 non-null   object \n",
      " 4   Salary             1000 non-null   int64  \n",
      " 5   Bonus %            1000 non-null   float64\n",
      " 6   Senior Management  933 non-null    object \n",
      " 7   Team               957 non-null    object \n",
      "dtypes: float64(1), int64(1), object(6)\n",
      "memory usage: 62.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "9da5f8e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.to_datetime converts argument to datetime.\n",
    "\n",
    "df[\"Start Date\"] = pd.to_datetime(df[\"Start Date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "fb4ba984",
   "metadata": {},
   "outputs": [],
   "source": [
    "# current date is prefixed with the time\n",
    "\n",
    "df[\"Last Login Time\"] = pd.to_datetime(df[\"Last Login Time\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "7e7a7197",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Senior Management\"] = df[\"Senior Management\"].astype(\"bool\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "208e3680",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Gender\"] = df[\"Gender\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "bae8ce40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>1993-08-06</td>\n",
       "      <td>2023-02-06 12:42:00</td>\n",
       "      <td>97308</td>\n",
       "      <td>6.945</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>1996-03-31</td>\n",
       "      <td>2023-02-06 06:53:00</td>\n",
       "      <td>61933</td>\n",
       "      <td>4.170</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>1993-04-23</td>\n",
       "      <td>2023-02-06 11:17:00</td>\n",
       "      <td>130590</td>\n",
       "      <td>11.858</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "0    Douglas    Male 1993-08-06 2023-02-06 12:42:00   97308    6.945   \n",
       "1     Thomas    Male 1996-03-31 2023-02-06 06:53:00   61933    4.170   \n",
       "2      Maria  Female 1993-04-23 2023-02-06 11:17:00  130590   11.858   \n",
       "\n",
       "   Senior Management       Team  \n",
       "0               True  Marketing  \n",
       "1               True        NaN  \n",
       "2              False    Finance  "
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "69859c19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 8 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   First Name         933 non-null    object        \n",
      " 1   Gender             855 non-null    category      \n",
      " 2   Start Date         1000 non-null   datetime64[ns]\n",
      " 3   Last Login Time    1000 non-null   datetime64[ns]\n",
      " 4   Salary             1000 non-null   int64         \n",
      " 5   Bonus %            1000 non-null   float64       \n",
      " 6   Senior Management  1000 non-null   bool          \n",
      " 7   Team               957 non-null    object        \n",
      "dtypes: bool(1), category(1), datetime64[ns](2), float64(1), int64(1), object(2)\n",
      "memory usage: 49.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# memory usage reduced\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1784dc30",
   "metadata": {},
   "source": [
    "### <font color = blue> Filter a dataframe based on one condition </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "960e93e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>1993-08-06</td>\n",
       "      <td>2023-02-06 12:42:00</td>\n",
       "      <td>97308</td>\n",
       "      <td>6.945</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>1996-03-31</td>\n",
       "      <td>2023-02-06 06:53:00</td>\n",
       "      <td>61933</td>\n",
       "      <td>4.170</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>1993-04-23</td>\n",
       "      <td>2023-02-06 11:17:00</td>\n",
       "      <td>130590</td>\n",
       "      <td>11.858</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>2005-03-04</td>\n",
       "      <td>2023-02-06 13:00:00</td>\n",
       "      <td>138705</td>\n",
       "      <td>9.340</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>1998-01-24</td>\n",
       "      <td>2023-02-06 16:47:00</td>\n",
       "      <td>101004</td>\n",
       "      <td>1.389</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "0    Douglas    Male 1993-08-06 2023-02-06 12:42:00   97308    6.945   \n",
       "1     Thomas    Male 1996-03-31 2023-02-06 06:53:00   61933    4.170   \n",
       "2      Maria  Female 1993-04-23 2023-02-06 11:17:00  130590   11.858   \n",
       "3      Jerry    Male 2005-03-04 2023-02-06 13:00:00  138705    9.340   \n",
       "4      Larry    Male 1998-01-24 2023-02-06 16:47:00  101004    1.389   \n",
       "\n",
       "   Senior Management             Team  \n",
       "0               True        Marketing  \n",
       "1               True              NaN  \n",
       "2              False          Finance  \n",
       "3               True          Finance  \n",
       "4               True  Client Services  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all above code in a single cell\n",
    "\n",
    "df = pd.read_csv(\"datasets/employees.csv\", parse_dates = [\"Start Date\", \"Last Login Time\"])\n",
    "\n",
    "df[\"Senior Management\"] = df[\"Senior Management\"].astype(\"bool\")\n",
    "df[\"Gender\"] = df[\"Gender\"].astype(\"category\")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "23fe7a7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2       True\n",
       "3       True\n",
       "4      False\n",
       "       ...  \n",
       "995    False\n",
       "996     True\n",
       "997    False\n",
       "998    False\n",
       "999    False\n",
       "Name: Team, Length: 1000, dtype: bool"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's suppose we want to filter the Team having Finance\n",
    "# below code will return boolean series, broadcasting operation is happening\n",
    "\n",
    "df[\"Team\"] == \"Finance\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "65140a02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>1993-04-23</td>\n",
       "      <td>2023-02-06 11:17:00</td>\n",
       "      <td>130590</td>\n",
       "      <td>11.858</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>2005-03-04</td>\n",
       "      <td>2023-02-06 13:00:00</td>\n",
       "      <td>138705</td>\n",
       "      <td>9.340</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>2015-07-20</td>\n",
       "      <td>2023-02-06 10:43:00</td>\n",
       "      <td>45906</td>\n",
       "      <td>11.598</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kimberly</td>\n",
       "      <td>Female</td>\n",
       "      <td>1999-01-14</td>\n",
       "      <td>2023-02-06 07:13:00</td>\n",
       "      <td>41426</td>\n",
       "      <td>14.543</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Bruce</td>\n",
       "      <td>Male</td>\n",
       "      <td>2009-11-28</td>\n",
       "      <td>2023-02-06 22:47:00</td>\n",
       "      <td>114796</td>\n",
       "      <td>6.796</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>907</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>Female</td>\n",
       "      <td>1998-07-27</td>\n",
       "      <td>2023-02-06 11:12:00</td>\n",
       "      <td>137144</td>\n",
       "      <td>10.081</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954</th>\n",
       "      <td>Joe</td>\n",
       "      <td>Male</td>\n",
       "      <td>1980-01-19</td>\n",
       "      <td>2023-02-06 16:06:00</td>\n",
       "      <td>119667</td>\n",
       "      <td>1.148</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>Gloria</td>\n",
       "      <td>Female</td>\n",
       "      <td>2014-12-08</td>\n",
       "      <td>2023-02-06 05:08:00</td>\n",
       "      <td>136709</td>\n",
       "      <td>10.331</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>Anthony</td>\n",
       "      <td>Male</td>\n",
       "      <td>2011-10-16</td>\n",
       "      <td>2023-02-06 08:35:00</td>\n",
       "      <td>112769</td>\n",
       "      <td>11.625</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Phillip</td>\n",
       "      <td>Male</td>\n",
       "      <td>1984-01-31</td>\n",
       "      <td>2023-02-06 06:30:00</td>\n",
       "      <td>42392</td>\n",
       "      <td>19.675</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "2        Maria  Female 1993-04-23 2023-02-06 11:17:00  130590   11.858   \n",
       "3        Jerry    Male 2005-03-04 2023-02-06 13:00:00  138705    9.340   \n",
       "7          NaN  Female 2015-07-20 2023-02-06 10:43:00   45906   11.598   \n",
       "14    Kimberly  Female 1999-01-14 2023-02-06 07:13:00   41426   14.543   \n",
       "46       Bruce    Male 2009-11-28 2023-02-06 22:47:00  114796    6.796   \n",
       "..         ...     ...        ...                 ...     ...      ...   \n",
       "907  Elizabeth  Female 1998-07-27 2023-02-06 11:12:00  137144   10.081   \n",
       "954        Joe    Male 1980-01-19 2023-02-06 16:06:00  119667    1.148   \n",
       "987     Gloria  Female 2014-12-08 2023-02-06 05:08:00  136709   10.331   \n",
       "992    Anthony    Male 2011-10-16 2023-02-06 08:35:00  112769   11.625   \n",
       "996    Phillip    Male 1984-01-31 2023-02-06 06:30:00   42392   19.675   \n",
       "\n",
       "     Senior Management     Team  \n",
       "2                False  Finance  \n",
       "3                 True  Finance  \n",
       "7                 True  Finance  \n",
       "14                True  Finance  \n",
       "46               False  Finance  \n",
       "..                 ...      ...  \n",
       "907              False  Finance  \n",
       "954               True  Finance  \n",
       "987               True  Finance  \n",
       "992               True  Finance  \n",
       "996              False  Finance  \n",
       "\n",
       "[102 rows x 8 columns]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# writing like this can confuse sometimes, instead use variable for the boolean series\n",
    "# pandas will extract only True value row from the boolean series\n",
    "\n",
    "df[df[\"Team\"] == \"Finance\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "b5edbb43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>2005-03-04</td>\n",
       "      <td>2023-02-06 13:00:00</td>\n",
       "      <td>138705</td>\n",
       "      <td>9.340</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>2015-07-20</td>\n",
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       "      <td>45906</td>\n",
       "      <td>11.598</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Angela</td>\n",
       "      <td>Female</td>\n",
       "      <td>2005-11-22</td>\n",
       "      <td>2023-02-06 06:29:00</td>\n",
       "      <td>95570</td>\n",
       "      <td>18.523</td>\n",
       "      <td>True</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Frances</td>\n",
       "      <td>Female</td>\n",
       "      <td>2002-08-08</td>\n",
       "      <td>2023-02-06 06:51:00</td>\n",
       "      <td>139852</td>\n",
       "      <td>7.524</td>\n",
       "      <td>True</td>\n",
       "      <td>Business Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gary</td>\n",
       "      <td>Male</td>\n",
       "      <td>2008-01-27</td>\n",
       "      <td>2023-02-06 23:40:00</td>\n",
       "      <td>109831</td>\n",
       "      <td>5.831</td>\n",
       "      <td>False</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "3       Jerry    Male 2005-03-04 2023-02-06 13:00:00  138705    9.340   \n",
       "7         NaN  Female 2015-07-20 2023-02-06 10:43:00   45906   11.598   \n",
       "8      Angela  Female 2005-11-22 2023-02-06 06:29:00   95570   18.523   \n",
       "9     Frances  Female 2002-08-08 2023-02-06 06:51:00  139852    7.524   \n",
       "13       Gary    Male 2008-01-27 2023-02-06 23:40:00  109831    5.831   \n",
       "\n",
       "    Senior Management                  Team  \n",
       "3                True               Finance  \n",
       "7                True               Finance  \n",
       "8                True           Engineering  \n",
       "9                True  Business Development  \n",
       "13              False                 Sales  "
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pandas will convert the str formated date to to_datetime itself,\n",
    "# but specifying date format is good practice\n",
    "\n",
    "mask = df[\"Start Date\"] > \"01/01/2000\"\n",
    "df[mask].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "8a142365",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Last Login Time</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
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       "      <td>97308</td>\n",
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       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
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       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>1993-04-23</td>\n",
       "      <td>2023-02-06 11:17:00</td>\n",
       "      <td>130590</td>\n",
       "      <td>11.858</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>2005-03-04</td>\n",
       "      <td>2023-02-06 13:00:00</td>\n",
       "      <td>138705</td>\n",
       "      <td>9.340</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>1998-01-24</td>\n",
       "      <td>2023-02-06 16:47:00</td>\n",
       "      <td>101004</td>\n",
       "      <td>1.389</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "0    Douglas    Male 1993-08-06 2023-02-06 12:42:00   97308    6.945   \n",
       "1     Thomas    Male 1996-03-31 2023-02-06 06:53:00   61933    4.170   \n",
       "2      Maria  Female 1993-04-23 2023-02-06 11:17:00  130590   11.858   \n",
       "3      Jerry    Male 2005-03-04 2023-02-06 13:00:00  138705    9.340   \n",
       "4      Larry    Male 1998-01-24 2023-02-06 16:47:00  101004    1.389   \n",
       "\n",
       "   Senior Management             Team  \n",
       "0               True        Marketing  \n",
       "1               True              NaN  \n",
       "2              False          Finance  \n",
       "3               True          Finance  \n",
       "4               True  Client Services  "
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = df[\"Team\"] != \"Sales\"\n",
    "df[mask].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e99a21a7",
   "metadata": {},
   "source": [
    "### <font color = blue> Filtering data with more than one condition </font>\n",
    "\n",
    "* And criteria filtering: `&`\n",
    "* Or criteria filtering: `|`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "ebbdf356",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>False</td>\n",
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       "      <td>138705</td>\n",
       "      <td>9.340</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>1998-01-24</td>\n",
       "      <td>2023-02-06 16:47:00</td>\n",
       "      <td>101004</td>\n",
       "      <td>1.389</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "0    Douglas    Male 1993-08-06 2023-02-06 12:42:00   97308    6.945   \n",
       "1     Thomas    Male 1996-03-31 2023-02-06 06:53:00   61933    4.170   \n",
       "2      Maria  Female 1993-04-23 2023-02-06 11:17:00  130590   11.858   \n",
       "3      Jerry    Male 2005-03-04 2023-02-06 13:00:00  138705    9.340   \n",
       "4      Larry    Male 1998-01-24 2023-02-06 16:47:00  101004    1.389   \n",
       "\n",
       "   Senior Management             Team  \n",
       "0               True        Marketing  \n",
       "1               True              NaN  \n",
       "2              False          Finance  \n",
       "3               True          Finance  \n",
       "4               True  Client Services  "
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "081cd5bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>21</th>\n",
       "      <td>Matthew</td>\n",
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       "      <td>1995-09-05</td>\n",
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       "      <td>False</td>\n",
       "      <td>Marketing</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Craig</td>\n",
       "      <td>Male</td>\n",
       "      <td>2000-02-27</td>\n",
       "      <td>2023-02-06 07:45:00</td>\n",
       "      <td>37598</td>\n",
       "      <td>7.757</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>1995-06-04</td>\n",
       "      <td>2023-02-06 14:24:00</td>\n",
       "      <td>62096</td>\n",
       "      <td>17.029</td>\n",
       "      <td>False</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Charles</td>\n",
       "      <td>Male</td>\n",
       "      <td>2004-09-14</td>\n",
       "      <td>2023-02-06 20:13:00</td>\n",
       "      <td>107391</td>\n",
       "      <td>1.260</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "0     Douglas   Male 1993-08-06 2023-02-06 12:42:00   97308    6.945   \n",
       "21    Matthew   Male 1995-09-05 2023-02-06 02:12:00  100612   13.645   \n",
       "26      Craig   Male 2000-02-27 2023-02-06 07:45:00   37598    7.757   \n",
       "74     Thomas   Male 1995-06-04 2023-02-06 14:24:00   62096   17.029   \n",
       "77    Charles   Male 2004-09-14 2023-02-06 20:13:00  107391    1.260   \n",
       "\n",
       "    Senior Management       Team  \n",
       "0                True  Marketing  \n",
       "21              False  Marketing  \n",
       "26               True  Marketing  \n",
       "74              False  Marketing  \n",
       "77               True  Marketing  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filtering Marketing Team having male employee\n",
    "\n",
    "mask1 = df[\"Team\"] == \"Marketing\"\n",
    "mask2 = df[\"Gender\"] == \"Male\"\n",
    "\n",
    "df[mask1 & mask2].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "ae9bbd6f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
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       "      <td>9.340</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>2015-07-20</td>\n",
       "      <td>2023-02-06 10:43:00</td>\n",
       "      <td>45906</td>\n",
       "      <td>11.598</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Angela</td>\n",
       "      <td>Female</td>\n",
       "      <td>2005-11-22</td>\n",
       "      <td>2023-02-06 06:29:00</td>\n",
       "      <td>95570</td>\n",
       "      <td>18.523</td>\n",
       "      <td>True</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gary</td>\n",
       "      <td>Male</td>\n",
       "      <td>2008-01-27</td>\n",
       "      <td>2023-02-06 23:40:00</td>\n",
       "      <td>109831</td>\n",
       "      <td>5.831</td>\n",
       "      <td>False</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Jeremy</td>\n",
       "      <td>Male</td>\n",
       "      <td>2010-09-21</td>\n",
       "      <td>2023-02-06 05:56:00</td>\n",
       "      <td>90370</td>\n",
       "      <td>7.369</td>\n",
       "      <td>False</td>\n",
       "      <td>Human Resources</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "3       Jerry    Male 2005-03-04 2023-02-06 13:00:00  138705    9.340   \n",
       "7         NaN  Female 2015-07-20 2023-02-06 10:43:00   45906   11.598   \n",
       "8      Angela  Female 2005-11-22 2023-02-06 06:29:00   95570   18.523   \n",
       "13       Gary    Male 2008-01-27 2023-02-06 23:40:00  109831    5.831   \n",
       "16     Jeremy    Male 2010-09-21 2023-02-06 05:56:00   90370    7.369   \n",
       "\n",
       "    Senior Management             Team  \n",
       "3                True          Finance  \n",
       "7                True          Finance  \n",
       "8                True      Engineering  \n",
       "13              False            Sales  \n",
       "16              False  Human Resources  "
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filtering employee having Bonus more than 5% or Salary greater than 100000 and Start Date is after 26th Jan, 2005\n",
    "\n",
    "mask1 = df[\"Bonus %\"] > 5\n",
    "mask2 = df[\"Salary\"] > 100000\n",
    "mask3 = df[\"Start Date\"] > pd.to_datetime(\"26/01/2005\", format = \"%d/%m/%Y\")  # this is a good practice, date format specification\n",
    "\n",
    "df[(mask1 | mask2) & mask3].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bcc3deb",
   "metadata": {},
   "source": [
    "### <font color=blue> Check for inclusion with the isin() method </font>\n",
    "\n",
    "* Whether each element in the DataFrame is contained in values.\n",
    "* values: 'Series | DataFrame | Sequence | Mapping'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "3dd3103d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>1993-08-06</td>\n",
       "      <td>2023-02-06 12:42:00</td>\n",
       "      <td>97308</td>\n",
       "      <td>6.945</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>Female</td>\n",
       "      <td>1987-08-17</td>\n",
       "      <td>2023-02-06 16:20:00</td>\n",
       "      <td>65476</td>\n",
       "      <td>10.012</td>\n",
       "      <td>True</td>\n",
       "      <td>Product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gary</td>\n",
       "      <td>Male</td>\n",
       "      <td>2008-01-27</td>\n",
       "      <td>2023-02-06 23:40:00</td>\n",
       "      <td>109831</td>\n",
       "      <td>5.831</td>\n",
       "      <td>False</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>2016-06-05</td>\n",
       "      <td>2023-02-06 06:09:00</td>\n",
       "      <td>59414</td>\n",
       "      <td>1.256</td>\n",
       "      <td>False</td>\n",
       "      <td>Product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Shawn</td>\n",
       "      <td>Male</td>\n",
       "      <td>1986-12-07</td>\n",
       "      <td>2023-02-06 19:45:00</td>\n",
       "      <td>111737</td>\n",
       "      <td>6.414</td>\n",
       "      <td>False</td>\n",
       "      <td>Product</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "0     Douglas    Male 1993-08-06 2023-02-06 12:42:00   97308    6.945   \n",
       "6        Ruby  Female 1987-08-17 2023-02-06 16:20:00   65476   10.012   \n",
       "13       Gary    Male 2008-01-27 2023-02-06 23:40:00  109831    5.831   \n",
       "15    Lillian  Female 2016-06-05 2023-02-06 06:09:00   59414    1.256   \n",
       "17      Shawn    Male 1986-12-07 2023-02-06 19:45:00  111737    6.414   \n",
       "\n",
       "    Senior Management       Team  \n",
       "0                True  Marketing  \n",
       "6                True    Product  \n",
       "13              False      Sales  \n",
       "15              False    Product  \n",
       "17              False    Product  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract all Team series having Sales, Product or Marketing\n",
    "\n",
    "mask1 = df[\"Team\"] == \"Sales\"\n",
    "mask2 = df[\"Team\"] == \"Product\"\n",
    "mask3 = df[\"Team\"] == \"Marketing\"\n",
    "\n",
    "df[mask1 | mask2 | mask3].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "29fb8902",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>1993-08-06</td>\n",
       "      <td>2023-02-06 12:42:00</td>\n",
       "      <td>97308</td>\n",
       "      <td>6.945</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ruby</td>\n",
       "      <td>Female</td>\n",
       "      <td>1987-08-17</td>\n",
       "      <td>2023-02-06 16:20:00</td>\n",
       "      <td>65476</td>\n",
       "      <td>10.012</td>\n",
       "      <td>True</td>\n",
       "      <td>Product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gary</td>\n",
       "      <td>Male</td>\n",
       "      <td>2008-01-27</td>\n",
       "      <td>2023-02-06 23:40:00</td>\n",
       "      <td>109831</td>\n",
       "      <td>5.831</td>\n",
       "      <td>False</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Lillian</td>\n",
       "      <td>Female</td>\n",
       "      <td>2016-06-05</td>\n",
       "      <td>2023-02-06 06:09:00</td>\n",
       "      <td>59414</td>\n",
       "      <td>1.256</td>\n",
       "      <td>False</td>\n",
       "      <td>Product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Shawn</td>\n",
       "      <td>Male</td>\n",
       "      <td>1986-12-07</td>\n",
       "      <td>2023-02-06 19:45:00</td>\n",
       "      <td>111737</td>\n",
       "      <td>6.414</td>\n",
       "      <td>False</td>\n",
       "      <td>Product</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "0     Douglas    Male 1993-08-06 2023-02-06 12:42:00   97308    6.945   \n",
       "6        Ruby  Female 1987-08-17 2023-02-06 16:20:00   65476   10.012   \n",
       "13       Gary    Male 2008-01-27 2023-02-06 23:40:00  109831    5.831   \n",
       "15    Lillian  Female 2016-06-05 2023-02-06 06:09:00   59414    1.256   \n",
       "17      Shawn    Male 1986-12-07 2023-02-06 19:45:00  111737    6.414   \n",
       "\n",
       "    Senior Management       Team  \n",
       "0                True  Marketing  \n",
       "6                True    Product  \n",
       "13              False      Sales  \n",
       "15              False    Product  \n",
       "17              False    Product  "
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# above implementation can be done using isin() method\n",
    "\n",
    "mask = df[\"Team\"].isin([\"Sales\", \"Product\", \"Marketing\"])\n",
    "df[mask].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "304f9d7d",
   "metadata": {},
   "source": [
    "### <font color=blue> Check for null values with the isnull() and notnull() methods </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "6fbc1b9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>1993-08-06</td>\n",
       "      <td>2023-02-06 12:42:00</td>\n",
       "      <td>97308</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>1996-03-31</td>\n",
       "      <td>2023-02-06 06:53:00</td>\n",
       "      <td>61933</td>\n",
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       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>1993-04-23</td>\n",
       "      <td>2023-02-06 11:17:00</td>\n",
       "      <td>130590</td>\n",
       "      <td>11.858</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>2005-03-04</td>\n",
       "      <td>2023-02-06 13:00:00</td>\n",
       "      <td>138705</td>\n",
       "      <td>9.340</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>1998-01-24</td>\n",
       "      <td>2023-02-06 16:47:00</td>\n",
       "      <td>101004</td>\n",
       "      <td>1.389</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "0    Douglas    Male 1993-08-06 2023-02-06 12:42:00   97308    6.945   \n",
       "1     Thomas    Male 1996-03-31 2023-02-06 06:53:00   61933    4.170   \n",
       "2      Maria  Female 1993-04-23 2023-02-06 11:17:00  130590   11.858   \n",
       "3      Jerry    Male 2005-03-04 2023-02-06 13:00:00  138705    9.340   \n",
       "4      Larry    Male 1998-01-24 2023-02-06 16:47:00  101004    1.389   \n",
       "\n",
       "   Senior Management             Team  \n",
       "0               True        Marketing  \n",
       "1               True              NaN  \n",
       "2              False          Finance  \n",
       "3               True          Finance  \n",
       "4               True  Client Services  "
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "854ef43c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1       True\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "995    False\n",
       "996    False\n",
       "997    False\n",
       "998    False\n",
       "999    False\n",
       "Name: Team, Length: 1000, dtype: bool"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Team\"].isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "6f349e17",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>Thomas</td>\n",
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       "      <th>10</th>\n",
       "      <td>Louise</td>\n",
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       "      <td>1980-08-12</td>\n",
       "      <td>2023-02-06 09:01:00</td>\n",
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       "      <td>125792</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
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       "      <td>2023-02-06 14:27:00</td>\n",
       "      <td>122340</td>\n",
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       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>91</th>\n",
       "      <td>James</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-01-26</td>\n",
       "      <td>2023-02-06 23:00:00</td>\n",
       "      <td>128771</td>\n",
       "      <td>8.309</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "1      Thomas    Male 1996-03-31 2023-02-06 06:53:00   61933    4.170   \n",
       "10     Louise  Female 1980-08-12 2023-02-06 09:01:00   63241   15.132   \n",
       "23        NaN    Male 2012-06-14 2023-02-06 16:19:00  125792    5.042   \n",
       "32        NaN    Male 1998-08-21 2023-02-06 14:27:00  122340    6.417   \n",
       "91      James     NaN 2005-01-26 2023-02-06 23:00:00  128771    8.309   \n",
       "\n",
       "    Senior Management Team  \n",
       "1                True  NaN  \n",
       "10               True  NaN  \n",
       "23               True  NaN  \n",
       "32               True  NaN  \n",
       "91              False  NaN  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[ df[\"Team\"].isnull() ].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "df72cd6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>1993-08-06</td>\n",
       "      <td>2023-02-06 12:42:00</td>\n",
       "      <td>97308</td>\n",
       "      <td>6.945</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>1993-04-23</td>\n",
       "      <td>2023-02-06 11:17:00</td>\n",
       "      <td>130590</td>\n",
       "      <td>11.858</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>2005-03-04</td>\n",
       "      <td>2023-02-06 13:00:00</td>\n",
       "      <td>138705</td>\n",
       "      <td>9.340</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>1998-01-24</td>\n",
       "      <td>2023-02-06 16:47:00</td>\n",
       "      <td>101004</td>\n",
       "      <td>1.389</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dennis</td>\n",
       "      <td>Male</td>\n",
       "      <td>1987-04-18</td>\n",
       "      <td>2023-02-06 01:35:00</td>\n",
       "      <td>115163</td>\n",
       "      <td>10.125</td>\n",
       "      <td>False</td>\n",
       "      <td>Legal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "0    Douglas    Male 1993-08-06 2023-02-06 12:42:00   97308    6.945   \n",
       "2      Maria  Female 1993-04-23 2023-02-06 11:17:00  130590   11.858   \n",
       "3      Jerry    Male 2005-03-04 2023-02-06 13:00:00  138705    9.340   \n",
       "4      Larry    Male 1998-01-24 2023-02-06 16:47:00  101004    1.389   \n",
       "5     Dennis    Male 1987-04-18 2023-02-06 01:35:00  115163   10.125   \n",
       "\n",
       "   Senior Management             Team  \n",
       "0               True        Marketing  \n",
       "2              False          Finance  \n",
       "3               True          Finance  \n",
       "4               True  Client Services  \n",
       "5              False            Legal  "
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[ df[\"Team\"].notnull() ].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc072a77",
   "metadata": {},
   "source": [
    "### <font color=blue> Check for inclusion within a range of values with the between() method </font>\n",
    "* Both ranges are inclusive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "60ace672",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>2012-10-08</td>\n",
       "      <td>2023-02-06 01:12:00</td>\n",
       "      <td>37076</td>\n",
       "      <td>18.576</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Craig</td>\n",
       "      <td>Male</td>\n",
       "      <td>2000-02-27</td>\n",
       "      <td>2023-02-06 07:45:00</td>\n",
       "      <td>37598</td>\n",
       "      <td>7.757</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Stephanie</td>\n",
       "      <td>Female</td>\n",
       "      <td>1986-09-13</td>\n",
       "      <td>2023-02-06 01:52:00</td>\n",
       "      <td>36844</td>\n",
       "      <td>5.574</td>\n",
       "      <td>True</td>\n",
       "      <td>Business Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Matthew</td>\n",
       "      <td>Male</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>2023-02-06 22:33:00</td>\n",
       "      <td>35203</td>\n",
       "      <td>18.040</td>\n",
       "      <td>False</td>\n",
       "      <td>Human Resources</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Steven</td>\n",
       "      <td>Male</td>\n",
       "      <td>1980-03-30</td>\n",
       "      <td>2023-02-06 21:20:00</td>\n",
       "      <td>35095</td>\n",
       "      <td>8.379</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "25        NaN    Male 2012-10-08 2023-02-06 01:12:00   37076   18.576   \n",
       "26      Craig    Male 2000-02-27 2023-02-06 07:45:00   37598    7.757   \n",
       "38  Stephanie  Female 1986-09-13 2023-02-06 01:52:00   36844    5.574   \n",
       "63    Matthew    Male 2013-01-02 2023-02-06 22:33:00   35203   18.040   \n",
       "82     Steven    Male 1980-03-30 2023-02-06 21:20:00   35095    8.379   \n",
       "\n",
       "    Senior Management                  Team  \n",
       "25               True       Client Services  \n",
       "26               True             Marketing  \n",
       "38               True  Business Development  \n",
       "63              False       Human Resources  \n",
       "82               True       Client Services  "
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = df[\"Salary\"].between(35000, 40000)\n",
    "df[mask].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "daf7d131",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Craig</td>\n",
       "      <td>Male</td>\n",
       "      <td>2000-02-27</td>\n",
       "      <td>2023-02-06 07:45:00</td>\n",
       "      <td>37598</td>\n",
       "      <td>7.757</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Nancy</td>\n",
       "      <td>Female</td>\n",
       "      <td>2000-09-23</td>\n",
       "      <td>2023-02-06 08:05:00</td>\n",
       "      <td>94976</td>\n",
       "      <td>13.830</td>\n",
       "      <td>True</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Linda</td>\n",
       "      <td>Female</td>\n",
       "      <td>2000-05-25</td>\n",
       "      <td>2023-02-06 17:45:00</td>\n",
       "      <td>119009</td>\n",
       "      <td>12.506</td>\n",
       "      <td>True</td>\n",
       "      <td>Business Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Christopher</td>\n",
       "      <td>Male</td>\n",
       "      <td>2000-04-22</td>\n",
       "      <td>2023-02-06 10:15:00</td>\n",
       "      <td>37919</td>\n",
       "      <td>11.449</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Kathy</td>\n",
       "      <td>Female</td>\n",
       "      <td>2000-03-18</td>\n",
       "      <td>2023-02-06 19:26:00</td>\n",
       "      <td>149563</td>\n",
       "      <td>16.991</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "26         Craig    Male 2000-02-27 2023-02-06 07:45:00   37598    7.757   \n",
       "50         Nancy  Female 2000-09-23 2023-02-06 08:05:00   94976   13.830   \n",
       "92         Linda  Female 2000-05-25 2023-02-06 17:45:00  119009   12.506   \n",
       "109  Christopher    Male 2000-04-22 2023-02-06 10:15:00   37919   11.449   \n",
       "160        Kathy  Female 2000-03-18 2023-02-06 19:26:00  149563   16.991   \n",
       "\n",
       "     Senior Management                  Team  \n",
       "26                True             Marketing  \n",
       "50                True           Engineering  \n",
       "92                True  Business Development  \n",
       "109              False                   NaN  \n",
       "160               True               Finance  "
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = df[\"Start Date\"].between(\"01-04-2000\", \"01-04-2001\")\n",
    "df[mask].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50a6fa74",
   "metadata": {},
   "source": [
    "### <font color=blue> Check for duplicate dataFrame rows with the duplicated() method </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "fe38d64c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>2012-02-17</td>\n",
       "      <td>2023-02-06 10:20:00</td>\n",
       "      <td>61602</td>\n",
       "      <td>11.849</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>1994-01-29</td>\n",
       "      <td>2023-02-06 18:48:00</td>\n",
       "      <td>58755</td>\n",
       "      <td>5.097</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>1990-07-22</td>\n",
       "      <td>2023-02-06 14:53:00</td>\n",
       "      <td>52119</td>\n",
       "      <td>11.343</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1986-01-22</td>\n",
       "      <td>2023-02-06 19:39:00</td>\n",
       "      <td>63126</td>\n",
       "      <td>18.424</td>\n",
       "      <td>False</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Adam</td>\n",
       "      <td>Male</td>\n",
       "      <td>2011-05-21</td>\n",
       "      <td>2023-02-06 01:45:00</td>\n",
       "      <td>95327</td>\n",
       "      <td>15.120</td>\n",
       "      <td>False</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "101      Aaron   Male 2012-02-17 2023-02-06 10:20:00   61602   11.849   \n",
       "327      Aaron   Male 1994-01-29 2023-02-06 18:48:00   58755    5.097   \n",
       "440      Aaron   Male 1990-07-22 2023-02-06 14:53:00   52119   11.343   \n",
       "937      Aaron    NaN 1986-01-22 2023-02-06 19:39:00   63126   18.424   \n",
       "137       Adam   Male 2011-05-21 2023-02-06 01:45:00   95327   15.120   \n",
       "\n",
       "     Senior Management             Team  \n",
       "101               True        Marketing  \n",
       "327               True        Marketing  \n",
       "440               True  Client Services  \n",
       "937              False  Client Services  \n",
       "137              False     Distribution  "
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.sort_values(by = \"First Name\")\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "d682d4f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101    False\n",
       "327     True\n",
       "440     True\n",
       "937     True\n",
       "137    False\n",
       "       ...  \n",
       "902     True\n",
       "925     True\n",
       "946     True\n",
       "947     True\n",
       "951     True\n",
       "Name: First Name, Length: 1000, dtype: bool"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"First Name\"].duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "d42505c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
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       "      <th>Team</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>1994-01-29</td>\n",
       "      <td>2023-02-06 18:48:00</td>\n",
       "      <td>58755</td>\n",
       "      <td>5.097</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>1990-07-22</td>\n",
       "      <td>2023-02-06 14:53:00</td>\n",
       "      <td>52119</td>\n",
       "      <td>11.343</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1986-01-22</td>\n",
       "      <td>2023-02-06 19:39:00</td>\n",
       "      <td>63126</td>\n",
       "      <td>18.424</td>\n",
       "      <td>False</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Adam</td>\n",
       "      <td>Male</td>\n",
       "      <td>1990-12-24</td>\n",
       "      <td>2023-02-06 20:57:00</td>\n",
       "      <td>110194</td>\n",
       "      <td>14.727</td>\n",
       "      <td>True</td>\n",
       "      <td>Product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>Adam</td>\n",
       "      <td>Male</td>\n",
       "      <td>2007-07-05</td>\n",
       "      <td>2023-02-06 11:59:00</td>\n",
       "      <td>71276</td>\n",
       "      <td>5.027</td>\n",
       "      <td>True</td>\n",
       "      <td>Human Resources</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>2001-05-23</td>\n",
       "      <td>2023-02-06 19:52:00</td>\n",
       "      <td>103877</td>\n",
       "      <td>6.322</td>\n",
       "      <td>True</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>2000-08-23</td>\n",
       "      <td>2023-02-06 16:19:00</td>\n",
       "      <td>95866</td>\n",
       "      <td>19.388</td>\n",
       "      <td>True</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>1985-09-15</td>\n",
       "      <td>2023-02-06 01:50:00</td>\n",
       "      <td>133472</td>\n",
       "      <td>16.941</td>\n",
       "      <td>True</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>2012-07-30</td>\n",
       "      <td>2023-02-06 15:07:00</td>\n",
       "      <td>107351</td>\n",
       "      <td>5.329</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>2010-09-14</td>\n",
       "      <td>2023-02-06 05:19:00</td>\n",
       "      <td>143638</td>\n",
       "      <td>9.662</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>799 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "327      Aaron    Male 1994-01-29 2023-02-06 18:48:00   58755    5.097   \n",
       "440      Aaron    Male 1990-07-22 2023-02-06 14:53:00   52119   11.343   \n",
       "937      Aaron     NaN 1986-01-22 2023-02-06 19:39:00   63126   18.424   \n",
       "141       Adam    Male 1990-12-24 2023-02-06 20:57:00  110194   14.727   \n",
       "302       Adam    Male 2007-07-05 2023-02-06 11:59:00   71276    5.027   \n",
       "..         ...     ...        ...                 ...     ...      ...   \n",
       "902        NaN    Male 2001-05-23 2023-02-06 19:52:00  103877    6.322   \n",
       "925        NaN  Female 2000-08-23 2023-02-06 16:19:00   95866   19.388   \n",
       "946        NaN  Female 1985-09-15 2023-02-06 01:50:00  133472   16.941   \n",
       "947        NaN    Male 2012-07-30 2023-02-06 15:07:00  107351    5.329   \n",
       "951        NaN  Female 2010-09-14 2023-02-06 05:19:00  143638    9.662   \n",
       "\n",
       "     Senior Management             Team  \n",
       "327               True        Marketing  \n",
       "440               True  Client Services  \n",
       "937              False  Client Services  \n",
       "141               True          Product  \n",
       "302               True  Human Resources  \n",
       "..                 ...              ...  \n",
       "902               True     Distribution  \n",
       "925               True            Sales  \n",
       "946               True     Distribution  \n",
       "947               True        Marketing  \n",
       "951               True              NaN  \n",
       "\n",
       "[799 rows x 8 columns]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mark duplicates as True except for the first occurrence.\n",
    "\n",
    "df1[ df1[\"First Name\"].duplicated() ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "66ca250e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101     True\n",
       "327     True\n",
       "440     True\n",
       "937    False\n",
       "137     True\n",
       "       ...  \n",
       "902     True\n",
       "925     True\n",
       "946     True\n",
       "947     True\n",
       "951    False\n",
       "Name: First Name, Length: 1000, dtype: bool"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"First Name\"].duplicated(keep = \"last\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "a3cd85a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
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       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>2012-02-17</td>\n",
       "      <td>2023-02-06 10:20:00</td>\n",
       "      <td>61602</td>\n",
       "      <td>11.849</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
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       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>Aaron</td>\n",
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       "      <td>1994-01-29</td>\n",
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       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>1990-07-22</td>\n",
       "      <td>2023-02-06 14:53:00</td>\n",
       "      <td>52119</td>\n",
       "      <td>11.343</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Adam</td>\n",
       "      <td>Male</td>\n",
       "      <td>2011-05-21</td>\n",
       "      <td>2023-02-06 01:45:00</td>\n",
       "      <td>95327</td>\n",
       "      <td>15.120</td>\n",
       "      <td>False</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Adam</td>\n",
       "      <td>Male</td>\n",
       "      <td>1990-12-24</td>\n",
       "      <td>2023-02-06 20:57:00</td>\n",
       "      <td>110194</td>\n",
       "      <td>14.727</td>\n",
       "      <td>True</td>\n",
       "      <td>Product</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>2015-11-24</td>\n",
       "      <td>2023-02-06 03:11:00</td>\n",
       "      <td>145329</td>\n",
       "      <td>7.100</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>2001-05-23</td>\n",
       "      <td>2023-02-06 19:52:00</td>\n",
       "      <td>103877</td>\n",
       "      <td>6.322</td>\n",
       "      <td>True</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>2000-08-23</td>\n",
       "      <td>2023-02-06 16:19:00</td>\n",
       "      <td>95866</td>\n",
       "      <td>19.388</td>\n",
       "      <td>True</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>1985-09-15</td>\n",
       "      <td>2023-02-06 01:50:00</td>\n",
       "      <td>133472</td>\n",
       "      <td>16.941</td>\n",
       "      <td>True</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>2012-07-30</td>\n",
       "      <td>2023-02-06 15:07:00</td>\n",
       "      <td>107351</td>\n",
       "      <td>5.329</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>799 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "101      Aaron    Male 2012-02-17 2023-02-06 10:20:00   61602   11.849   \n",
       "327      Aaron    Male 1994-01-29 2023-02-06 18:48:00   58755    5.097   \n",
       "440      Aaron    Male 1990-07-22 2023-02-06 14:53:00   52119   11.343   \n",
       "137       Adam    Male 2011-05-21 2023-02-06 01:45:00   95327   15.120   \n",
       "141       Adam    Male 1990-12-24 2023-02-06 20:57:00  110194   14.727   \n",
       "..         ...     ...        ...                 ...     ...      ...   \n",
       "890        NaN    Male 2015-11-24 2023-02-06 03:11:00  145329    7.100   \n",
       "902        NaN    Male 2001-05-23 2023-02-06 19:52:00  103877    6.322   \n",
       "925        NaN  Female 2000-08-23 2023-02-06 16:19:00   95866   19.388   \n",
       "946        NaN  Female 1985-09-15 2023-02-06 01:50:00  133472   16.941   \n",
       "947        NaN    Male 2012-07-30 2023-02-06 15:07:00  107351    5.329   \n",
       "\n",
       "     Senior Management             Team  \n",
       "101               True        Marketing  \n",
       "327               True        Marketing  \n",
       "440               True  Client Services  \n",
       "137              False     Distribution  \n",
       "141               True          Product  \n",
       "..                 ...              ...  \n",
       "890               True          Finance  \n",
       "902               True     Distribution  \n",
       "925               True            Sales  \n",
       "946               True     Distribution  \n",
       "947               True        Marketing  \n",
       "\n",
       "[799 rows x 8 columns]"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[ df1[\"First Name\"].duplicated(keep = \"last\") ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "22b63e18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101    True\n",
       "327    True\n",
       "440    True\n",
       "937    True\n",
       "137    True\n",
       "       ... \n",
       "902    True\n",
       "925    True\n",
       "946    True\n",
       "947    True\n",
       "951    True\n",
       "Name: First Name, Length: 1000, dtype: bool"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mark all duplicates as True\n",
    "\n",
    "df1[\"First Name\"].duplicated(keep = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "1ebbc97c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
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       "      <th>Last Login Time</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Angela</td>\n",
       "      <td>Female</td>\n",
       "      <td>2005-11-22</td>\n",
       "      <td>2023-02-06 06:29:00</td>\n",
       "      <td>95570</td>\n",
       "      <td>18.523</td>\n",
       "      <td>True</td>\n",
       "      <td>Engineering</td>\n",
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       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>Brian</td>\n",
       "      <td>Male</td>\n",
       "      <td>2007-04-07</td>\n",
       "      <td>2023-02-06 22:47:00</td>\n",
       "      <td>93901</td>\n",
       "      <td>17.821</td>\n",
       "      <td>True</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Carol</td>\n",
       "      <td>Female</td>\n",
       "      <td>1996-03-19</td>\n",
       "      <td>2023-02-06 03:39:00</td>\n",
       "      <td>57783</td>\n",
       "      <td>9.129</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>David</td>\n",
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       "      <td>2023-02-06 08:48:00</td>\n",
       "      <td>92242</td>\n",
       "      <td>15.407</td>\n",
       "      <td>False</td>\n",
       "      <td>Legal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dennis</td>\n",
       "      <td>Male</td>\n",
       "      <td>1987-04-18</td>\n",
       "      <td>2023-02-06 01:35:00</td>\n",
       "      <td>115163</td>\n",
       "      <td>10.125</td>\n",
       "      <td>False</td>\n",
       "      <td>Legal</td>\n",
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       "      <td>81077</td>\n",
       "      <td>2.117</td>\n",
       "      <td>False</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Jean</td>\n",
       "      <td>Female</td>\n",
       "      <td>1993-12-18</td>\n",
       "      <td>2023-02-06 09:07:00</td>\n",
       "      <td>119082</td>\n",
       "      <td>16.180</td>\n",
       "      <td>False</td>\n",
       "      <td>Business Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>Keith</td>\n",
       "      <td>Male</td>\n",
       "      <td>2003-02-12</td>\n",
       "      <td>2023-02-06 15:02:00</td>\n",
       "      <td>120672</td>\n",
       "      <td>19.467</td>\n",
       "      <td>False</td>\n",
       "      <td>Legal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>Tammy</td>\n",
       "      <td>Female</td>\n",
       "      <td>1984-11-11</td>\n",
       "      <td>2023-02-06 10:30:00</td>\n",
       "      <td>132839</td>\n",
       "      <td>17.463</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "8       Angela  Female 2005-11-22 2023-02-06 06:29:00   95570   18.523   \n",
       "688      Brian    Male 2007-04-07 2023-02-06 22:47:00   93901   17.821   \n",
       "190      Carol  Female 1996-03-19 2023-02-06 03:39:00   57783    9.129   \n",
       "887      David    Male 2009-12-05 2023-02-06 08:48:00   92242   15.407   \n",
       "5       Dennis    Male 1987-04-18 2023-02-06 01:35:00  115163   10.125   \n",
       "495     Eugene    Male 1984-05-24 2023-02-06 10:54:00   81077    2.117   \n",
       "33        Jean  Female 1993-12-18 2023-02-06 09:07:00  119082   16.180   \n",
       "832      Keith    Male 2003-02-12 2023-02-06 15:02:00  120672   19.467   \n",
       "291      Tammy  Female 1984-11-11 2023-02-06 10:30:00  132839   17.463   \n",
       "\n",
       "     Senior Management                  Team  \n",
       "8                 True           Engineering  \n",
       "688               True                 Legal  \n",
       "190              False               Finance  \n",
       "887              False                 Legal  \n",
       "5                False                 Legal  \n",
       "495              False                 Sales  \n",
       "33               False  Business Development  \n",
       "832              False                 Legal  \n",
       "291               True       Client Services  "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only unique First Name values left, even the first occurence of duplicate is removed\n",
    "# `~`: negation operation\n",
    "\n",
    "df1[ ~ df1[\"First Name\"].duplicated(keep = False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58816ff4",
   "metadata": {},
   "source": [
    "### <font color=blue> Delete duplicate dataFrame rows with the drop_duplicates() method </font>\n",
    "\n",
    "* Return DataFrame with duplicate rows removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "bd61e434",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>2012-02-17</td>\n",
       "      <td>2023-02-06 10:20:00</td>\n",
       "      <td>61602</td>\n",
       "      <td>11.849</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>1994-01-29</td>\n",
       "      <td>2023-02-06 18:48:00</td>\n",
       "      <td>58755</td>\n",
       "      <td>5.097</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
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       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>1990-07-22</td>\n",
       "      <td>2023-02-06 14:53:00</td>\n",
       "      <td>52119</td>\n",
       "      <td>11.343</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1986-01-22</td>\n",
       "      <td>2023-02-06 19:39:00</td>\n",
       "      <td>63126</td>\n",
       "      <td>18.424</td>\n",
       "      <td>False</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Adam</td>\n",
       "      <td>Male</td>\n",
       "      <td>2011-05-21</td>\n",
       "      <td>2023-02-06 01:45:00</td>\n",
       "      <td>95327</td>\n",
       "      <td>15.120</td>\n",
       "      <td>False</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "101      Aaron   Male 2012-02-17 2023-02-06 10:20:00   61602   11.849   \n",
       "327      Aaron   Male 1994-01-29 2023-02-06 18:48:00   58755    5.097   \n",
       "440      Aaron   Male 1990-07-22 2023-02-06 14:53:00   52119   11.343   \n",
       "937      Aaron    NaN 1986-01-22 2023-02-06 19:39:00   63126   18.424   \n",
       "137       Adam   Male 2011-05-21 2023-02-06 01:45:00   95327   15.120   \n",
       "\n",
       "     Senior Management             Team  \n",
       "101               True        Marketing  \n",
       "327               True        Marketing  \n",
       "440               True  Client Services  \n",
       "937              False  Client Services  \n",
       "137              False     Distribution  "
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "a2961ef7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "a5616327",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
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       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
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       "      <td>Aaron</td>\n",
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       "      <td>2023-02-06 10:20:00</td>\n",
       "      <td>61602</td>\n",
       "      <td>11.849</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
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       "      <th>327</th>\n",
       "      <td>Aaron</td>\n",
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       "      <td>58755</td>\n",
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       "      <td>Aaron</td>\n",
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       "      <td>2023-02-06 14:53:00</td>\n",
       "      <td>52119</td>\n",
       "      <td>11.343</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1986-01-22</td>\n",
       "      <td>2023-02-06 19:39:00</td>\n",
       "      <td>63126</td>\n",
       "      <td>18.424</td>\n",
       "      <td>False</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Adam</td>\n",
       "      <td>Male</td>\n",
       "      <td>2011-05-21</td>\n",
       "      <td>2023-02-06 01:45:00</td>\n",
       "      <td>95327</td>\n",
       "      <td>15.120</td>\n",
       "      <td>False</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "101      Aaron   Male 2012-02-17 2023-02-06 10:20:00   61602   11.849   \n",
       "327      Aaron   Male 1994-01-29 2023-02-06 18:48:00   58755    5.097   \n",
       "440      Aaron   Male 1990-07-22 2023-02-06 14:53:00   52119   11.343   \n",
       "937      Aaron    NaN 1986-01-22 2023-02-06 19:39:00   63126   18.424   \n",
       "137       Adam   Male 2011-05-21 2023-02-06 01:45:00   95327   15.120   \n",
       "\n",
       "     Senior Management             Team  \n",
       "101               True        Marketing  \n",
       "327               True        Marketing  \n",
       "440               True  Client Services  \n",
       "937              False  Client Services  \n",
       "137              False     Distribution  "
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.drop_duplicates().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "eadb7de9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as there're no duplicate rows ie. two rows having same col values entirely\n",
    "\n",
    "len(df1.drop_duplicates())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "46cbdf3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>101</th>\n",
       "      <td>Aaron</td>\n",
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       "      <td>61602</td>\n",
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       "      <td>True</td>\n",
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       "      <th>137</th>\n",
       "      <td>Adam</td>\n",
       "      <td>Male</td>\n",
       "      <td>2011-05-21</td>\n",
       "      <td>2023-02-06 01:45:00</td>\n",
       "      <td>95327</td>\n",
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       "      <td>False</td>\n",
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       "      <th>300</th>\n",
       "      <td>Alan</td>\n",
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       "      <td>1988-06-26</td>\n",
       "      <td>2023-02-06 03:54:00</td>\n",
       "      <td>111786</td>\n",
       "      <td>3.592</td>\n",
       "      <td>True</td>\n",
       "      <td>Engineering</td>\n",
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       "      <th>372</th>\n",
       "      <td>Albert</td>\n",
       "      <td>Male</td>\n",
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       "      <td>2023-02-06 16:20:00</td>\n",
       "      <td>67827</td>\n",
       "      <td>19.717</td>\n",
       "      <td>True</td>\n",
       "      <td>Engineering</td>\n",
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       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>Alice</td>\n",
       "      <td>Female</td>\n",
       "      <td>2004-10-05</td>\n",
       "      <td>2023-02-06 09:34:00</td>\n",
       "      <td>47638</td>\n",
       "      <td>11.209</td>\n",
       "      <td>False</td>\n",
       "      <td>Human Resources</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>Wanda</td>\n",
       "      <td>Female</td>\n",
       "      <td>2008-07-20</td>\n",
       "      <td>2023-02-06 13:44:00</td>\n",
       "      <td>65362</td>\n",
       "      <td>7.132</td>\n",
       "      <td>True</td>\n",
       "      <td>Legal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Wayne</td>\n",
       "      <td>Male</td>\n",
       "      <td>2012-04-07</td>\n",
       "      <td>2023-02-06 08:00:00</td>\n",
       "      <td>102652</td>\n",
       "      <td>14.085</td>\n",
       "      <td>True</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>William</td>\n",
       "      <td>Male</td>\n",
       "      <td>1993-11-18</td>\n",
       "      <td>2023-02-06 12:27:00</td>\n",
       "      <td>54058</td>\n",
       "      <td>5.182</td>\n",
       "      <td>True</td>\n",
       "      <td>Human Resources</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>Willie</td>\n",
       "      <td>Male</td>\n",
       "      <td>2009-08-22</td>\n",
       "      <td>2023-02-06 13:03:00</td>\n",
       "      <td>55038</td>\n",
       "      <td>19.691</td>\n",
       "      <td>False</td>\n",
       "      <td>Legal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>2015-07-20</td>\n",
       "      <td>2023-02-06 10:43:00</td>\n",
       "      <td>45906</td>\n",
       "      <td>11.598</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "101      Aaron    Male 2012-02-17 2023-02-06 10:20:00   61602   11.849   \n",
       "137       Adam    Male 2011-05-21 2023-02-06 01:45:00   95327   15.120   \n",
       "300       Alan    Male 1988-06-26 2023-02-06 03:54:00  111786    3.592   \n",
       "372     Albert    Male 1997-02-01 2023-02-06 16:20:00   67827   19.717   \n",
       "988      Alice  Female 2004-10-05 2023-02-06 09:34:00   47638   11.209   \n",
       "..         ...     ...        ...                 ...     ...      ...   \n",
       "433      Wanda  Female 2008-07-20 2023-02-06 13:44:00   65362    7.132   \n",
       "177      Wayne    Male 2012-04-07 2023-02-06 08:00:00  102652   14.085   \n",
       "820    William    Male 1993-11-18 2023-02-06 12:27:00   54058    5.182   \n",
       "450     Willie    Male 2009-08-22 2023-02-06 13:03:00   55038   19.691   \n",
       "7          NaN  Female 2015-07-20 2023-02-06 10:43:00   45906   11.598   \n",
       "\n",
       "     Senior Management             Team  \n",
       "101               True        Marketing  \n",
       "137              False     Distribution  \n",
       "300               True      Engineering  \n",
       "372               True      Engineering  \n",
       "988              False  Human Resources  \n",
       "..                 ...              ...  \n",
       "433               True            Legal  \n",
       "177               True     Distribution  \n",
       "820               True  Human Resources  \n",
       "450              False            Legal  \n",
       "7                 True          Finance  \n",
       "\n",
       "[201 rows x 8 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# keep first duplicate and remove others\n",
    "\n",
    "df1.drop_duplicates(subset = [\"First Name\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "0c31df53",
   "metadata": {},
   "outputs": [
    {
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       "      <th>538</th>\n",
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       "      <td>2023-02-06 00:26:00</td>\n",
       "      <td>41453</td>\n",
       "      <td>10.084</td>\n",
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       "      <td>Product</td>\n",
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       "      <th>959</th>\n",
       "      <td>Albert</td>\n",
       "      <td>Male</td>\n",
       "      <td>1992-09-19</td>\n",
       "      <td>2023-02-06 02:35:00</td>\n",
       "      <td>45094</td>\n",
       "      <td>5.850</td>\n",
       "      <td>True</td>\n",
       "      <td>Business Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>Alice</td>\n",
       "      <td>Female</td>\n",
       "      <td>1995-10-16</td>\n",
       "      <td>2023-02-06 21:19:00</td>\n",
       "      <td>92799</td>\n",
       "      <td>2.782</td>\n",
       "      <td>False</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>Wanda</td>\n",
       "      <td>Female</td>\n",
       "      <td>1993-04-06</td>\n",
       "      <td>2023-02-06 03:11:00</td>\n",
       "      <td>78883</td>\n",
       "      <td>19.695</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>Wayne</td>\n",
       "      <td>Male</td>\n",
       "      <td>2009-09-02</td>\n",
       "      <td>2023-02-06 01:37:00</td>\n",
       "      <td>126956</td>\n",
       "      <td>18.396</td>\n",
       "      <td>False</td>\n",
       "      <td>Human Resources</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>William</td>\n",
       "      <td>Male</td>\n",
       "      <td>2002-09-29</td>\n",
       "      <td>2023-02-06 16:09:00</td>\n",
       "      <td>66521</td>\n",
       "      <td>5.830</td>\n",
       "      <td>False</td>\n",
       "      <td>Human Resources</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>Willie</td>\n",
       "      <td>Male</td>\n",
       "      <td>2009-12-05</td>\n",
       "      <td>2023-02-06 05:39:00</td>\n",
       "      <td>141932</td>\n",
       "      <td>1.017</td>\n",
       "      <td>True</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>951</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>2010-09-14</td>\n",
       "      <td>2023-02-06 05:19:00</td>\n",
       "      <td>143638</td>\n",
       "      <td>9.662</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "937      Aaron     NaN 1986-01-22 2023-02-06 19:39:00   63126   18.424   \n",
       "538       Adam    Male 2010-10-08 2023-02-06 21:53:00   45181    3.491   \n",
       "610       Alan    Male 2012-02-17 2023-02-06 00:26:00   41453   10.084   \n",
       "959     Albert    Male 1992-09-19 2023-02-06 02:35:00   45094    5.850   \n",
       "693      Alice  Female 1995-10-16 2023-02-06 21:19:00   92799    2.782   \n",
       "..         ...     ...        ...                 ...     ...      ...   \n",
       "512      Wanda  Female 1993-04-06 2023-02-06 03:11:00   78883   19.695   \n",
       "637      Wayne    Male 2009-09-02 2023-02-06 01:37:00  126956   18.396   \n",
       "127    William    Male 2002-09-29 2023-02-06 16:09:00   66521    5.830   \n",
       "652     Willie    Male 2009-12-05 2023-02-06 05:39:00  141932    1.017   \n",
       "951        NaN  Female 2010-09-14 2023-02-06 05:19:00  143638    9.662   \n",
       "\n",
       "     Senior Management                  Team  \n",
       "937              False       Client Services  \n",
       "538              False       Human Resources  \n",
       "610              False               Product  \n",
       "959               True  Business Development  \n",
       "693              False                 Sales  \n",
       "..                 ...                   ...  \n",
       "512              False                   NaN  \n",
       "637              False       Human Resources  \n",
       "127              False       Human Resources  \n",
       "652               True           Engineering  \n",
       "951               True                   NaN  \n",
       "\n",
       "[201 rows x 8 columns]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# keep last duplicate value and remove others\n",
    "\n",
    "df1.drop_duplicates(subset = [\"First Name\"], keep = \"last\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "2a6a3439",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>Male</td>\n",
       "      <td>2012-02-17</td>\n",
       "      <td>2023-02-06 10:20:00</td>\n",
       "      <td>61602</td>\n",
       "      <td>11.849</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>Aaron</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1986-01-22</td>\n",
       "      <td>2023-02-06 19:39:00</td>\n",
       "      <td>63126</td>\n",
       "      <td>18.424</td>\n",
       "      <td>False</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Adam</td>\n",
       "      <td>Male</td>\n",
       "      <td>2011-05-21</td>\n",
       "      <td>2023-02-06 01:45:00</td>\n",
       "      <td>95327</td>\n",
       "      <td>15.120</td>\n",
       "      <td>False</td>\n",
       "      <td>Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Adam</td>\n",
       "      <td>Male</td>\n",
       "      <td>1990-12-24</td>\n",
       "      <td>2023-02-06 20:57:00</td>\n",
       "      <td>110194</td>\n",
       "      <td>14.727</td>\n",
       "      <td>True</td>\n",
       "      <td>Product</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>Alan</td>\n",
       "      <td>Male</td>\n",
       "      <td>1988-06-26</td>\n",
       "      <td>2023-02-06 03:54:00</td>\n",
       "      <td>111786</td>\n",
       "      <td>3.592</td>\n",
       "      <td>True</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>William</td>\n",
       "      <td>Male</td>\n",
       "      <td>1993-11-18</td>\n",
       "      <td>2023-02-06 12:27:00</td>\n",
       "      <td>54058</td>\n",
       "      <td>5.182</td>\n",
       "      <td>True</td>\n",
       "      <td>Human Resources</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>William</td>\n",
       "      <td>Male</td>\n",
       "      <td>2002-09-29</td>\n",
       "      <td>2023-02-06 16:09:00</td>\n",
       "      <td>66521</td>\n",
       "      <td>5.830</td>\n",
       "      <td>False</td>\n",
       "      <td>Human Resources</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>Willie</td>\n",
       "      <td>Male</td>\n",
       "      <td>2009-08-22</td>\n",
       "      <td>2023-02-06 13:03:00</td>\n",
       "      <td>55038</td>\n",
       "      <td>19.691</td>\n",
       "      <td>False</td>\n",
       "      <td>Legal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Willie</td>\n",
       "      <td>Male</td>\n",
       "      <td>1998-02-17</td>\n",
       "      <td>2023-02-06 20:20:00</td>\n",
       "      <td>146651</td>\n",
       "      <td>1.451</td>\n",
       "      <td>True</td>\n",
       "      <td>Engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>2015-07-20</td>\n",
       "      <td>2023-02-06 10:43:00</td>\n",
       "      <td>45906</td>\n",
       "      <td>11.598</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "101      Aaron    Male 2012-02-17 2023-02-06 10:20:00   61602   11.849   \n",
       "937      Aaron     NaN 1986-01-22 2023-02-06 19:39:00   63126   18.424   \n",
       "137       Adam    Male 2011-05-21 2023-02-06 01:45:00   95327   15.120   \n",
       "141       Adam    Male 1990-12-24 2023-02-06 20:57:00  110194   14.727   \n",
       "300       Alan    Male 1988-06-26 2023-02-06 03:54:00  111786    3.592   \n",
       "..         ...     ...        ...                 ...     ...      ...   \n",
       "820    William    Male 1993-11-18 2023-02-06 12:27:00   54058    5.182   \n",
       "127    William    Male 2002-09-29 2023-02-06 16:09:00   66521    5.830   \n",
       "450     Willie    Male 2009-08-22 2023-02-06 13:03:00   55038   19.691   \n",
       "175     Willie    Male 1998-02-17 2023-02-06 20:20:00  146651    1.451   \n",
       "7          NaN  Female 2015-07-20 2023-02-06 10:43:00   45906   11.598   \n",
       "\n",
       "     Senior Management             Team  \n",
       "101               True        Marketing  \n",
       "937              False  Client Services  \n",
       "137              False     Distribution  \n",
       "141               True          Product  \n",
       "300               True      Engineering  \n",
       "..                 ...              ...  \n",
       "820               True  Human Resources  \n",
       "127              False  Human Resources  \n",
       "450              False            Legal  \n",
       "175               True      Engineering  \n",
       "7                 True          Finance  \n",
       "\n",
       "[365 rows x 8 columns]"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# both providing col values are check for duplication, ie (r1.c1, r1.c2) should not same as (r2.c1, r2.c2)\n",
    "\n",
    "df1.drop_duplicates(subset = [\"First Name\", \"Senior Management\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ddd811e",
   "metadata": {},
   "source": [
    "### <font color=blue> Identify and count unique values with the unique() and nunique() methods </font>\n",
    "\n",
    "* unique(): Return unique values of Series object\n",
    "* nunique(): Count number of distinct elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "c7e49a8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>Last Login Time</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "      <th>Senior Management</th>\n",
       "      <th>Team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Douglas</td>\n",
       "      <td>Male</td>\n",
       "      <td>1993-08-06</td>\n",
       "      <td>2023-02-06 12:42:00</td>\n",
       "      <td>97308</td>\n",
       "      <td>6.945</td>\n",
       "      <td>True</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Male</td>\n",
       "      <td>1996-03-31</td>\n",
       "      <td>2023-02-06 06:53:00</td>\n",
       "      <td>61933</td>\n",
       "      <td>4.170</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maria</td>\n",
       "      <td>Female</td>\n",
       "      <td>1993-04-23</td>\n",
       "      <td>2023-02-06 11:17:00</td>\n",
       "      <td>130590</td>\n",
       "      <td>11.858</td>\n",
       "      <td>False</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jerry</td>\n",
       "      <td>Male</td>\n",
       "      <td>2005-03-04</td>\n",
       "      <td>2023-02-06 13:00:00</td>\n",
       "      <td>138705</td>\n",
       "      <td>9.340</td>\n",
       "      <td>True</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Larry</td>\n",
       "      <td>Male</td>\n",
       "      <td>1998-01-24</td>\n",
       "      <td>2023-02-06 16:47:00</td>\n",
       "      <td>101004</td>\n",
       "      <td>1.389</td>\n",
       "      <td>True</td>\n",
       "      <td>Client Services</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First Name  Gender Start Date     Last Login Time  Salary  Bonus %  \\\n",
       "0    Douglas    Male 1993-08-06 2023-02-06 12:42:00   97308    6.945   \n",
       "1     Thomas    Male 1996-03-31 2023-02-06 06:53:00   61933    4.170   \n",
       "2      Maria  Female 1993-04-23 2023-02-06 11:17:00  130590   11.858   \n",
       "3      Jerry    Male 2005-03-04 2023-02-06 13:00:00  138705    9.340   \n",
       "4      Larry    Male 1998-01-24 2023-02-06 16:47:00  101004    1.389   \n",
       "\n",
       "   Senior Management             Team  \n",
       "0               True        Marketing  \n",
       "1               True              NaN  \n",
       "2              False          Finance  \n",
       "3               True          Finance  \n",
       "4               True  Client Services  "
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "6f2f121e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Male', 'Female', NaN]\n",
       "Categories (2, object): ['Female', 'Male']"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Gender\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "fd900d32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unique method also consider NaN value, whereas nunique method doesn't\n",
    "\n",
    "df[\"Gender\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "fdf94751",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to consider nan also\n",
    "\n",
    "df[\"Gender\"].nunique(dropna = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b40e5509",
   "metadata": {},
   "source": [
    "## <font color=fe7401> Data Extraction </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "d85f4b61",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Film</th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dr. No</td>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>From Russia with Love</td>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Goldfinger</td>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thunderball</td>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Casino Royale</td>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Film  Year         Actor       Director  Box Office  \\\n",
       "0                 Dr. No  1962  Sean Connery  Terence Young       448.8   \n",
       "1  From Russia with Love  1963  Sean Connery  Terence Young       543.8   \n",
       "2             Goldfinger  1964  Sean Connery   Guy Hamilton       820.4   \n",
       "3            Thunderball  1965  Sean Connery  Terence Young       848.1   \n",
       "4          Casino Royale  1967   David Niven     Ken Hughes       315.0   \n",
       "\n",
       "   Budget  Bond Actor Salary  \n",
       "0     7.0                0.6  \n",
       "1    12.6                1.6  \n",
       "2    18.6                3.2  \n",
       "3    41.9                4.7  \n",
       "4    85.0                NaN  "
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond = pd.read_csv(\"./datasets/jamesbond.csv\")\n",
    "bond.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb0884e",
   "metadata": {},
   "source": [
    "### <font color=blue> Use the set_index and reset_index methods to define a new dataFrame index </font>\n",
    "\n",
    "* set_index(): set the DataFrame index using existing columns.\n",
    "* reset_index(): reset the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "f2e7a11f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year         Actor       Director  Box Office  Budget  \\\n",
       "Film                                                                           \n",
       "Dr. No                 1962  Sean Connery  Terence Young       448.8     7.0   \n",
       "From Russia with Love  1963  Sean Connery  Terence Young       543.8    12.6   \n",
       "Goldfinger             1964  Sean Connery   Guy Hamilton       820.4    18.6   \n",
       "Thunderball            1965  Sean Connery  Terence Young       848.1    41.9   \n",
       "Casino Royale          1967   David Niven     Ken Hughes       315.0    85.0   \n",
       "\n",
       "                       Bond Actor Salary  \n",
       "Film                                      \n",
       "Dr. No                               0.6  \n",
       "From Russia with Love                1.6  \n",
       "Goldfinger                           3.2  \n",
       "Thunderball                          4.7  \n",
       "Casino Royale                        NaN  "
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# similar to >>> bond = pd.read_csv(\"./datasets/jamesbond.csv\", index_col = \"Film\")\n",
    "\n",
    "bond.set_index(\"Film\", inplace = True)\n",
    "bond.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "b0eb05d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Actor       Director  Box Office  Budget  Bond Actor Salary\n",
       "Year                                                                    \n",
       "1962  Sean Connery  Terence Young       448.8     7.0                0.6\n",
       "1963  Sean Connery  Terence Young       543.8    12.6                1.6\n",
       "1964  Sean Connery   Guy Hamilton       820.4    18.6                3.2\n",
       "1965  Sean Connery  Terence Young       848.1    41.9                4.7\n",
       "1967   David Niven     Ken Hughes       315.0    85.0                NaN"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# default drop -> True, drops the previous index col entirely\n",
    "\n",
    "bond.set_index(\"Year\").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "3c9847c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year         Actor       Director  Box Office  Budget  \\\n",
       "Film                                                                           \n",
       "Dr. No                 1962  Sean Connery  Terence Young       448.8     7.0   \n",
       "From Russia with Love  1963  Sean Connery  Terence Young       543.8    12.6   \n",
       "Goldfinger             1964  Sean Connery   Guy Hamilton       820.4    18.6   \n",
       "Thunderball            1965  Sean Connery  Terence Young       848.1    41.9   \n",
       "Casino Royale          1967   David Niven     Ken Hughes       315.0    85.0   \n",
       "\n",
       "                       Bond Actor Salary  \n",
       "Film                                      \n",
       "Dr. No                               0.6  \n",
       "From Russia with Love                1.6  \n",
       "Goldfinger                           3.2  \n",
       "Thunderball                          4.7  \n",
       "Casino Royale                        NaN  "
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "f363f9d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
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       "      <th>0</th>\n",
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       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>From Russia with Love</td>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Goldfinger</td>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thunderball</td>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Casino Royale</td>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Film  Year         Actor       Director  Box Office  \\\n",
       "0                 Dr. No  1962  Sean Connery  Terence Young       448.8   \n",
       "1  From Russia with Love  1963  Sean Connery  Terence Young       543.8   \n",
       "2             Goldfinger  1964  Sean Connery   Guy Hamilton       820.4   \n",
       "3            Thunderball  1965  Sean Connery  Terence Young       848.1   \n",
       "4          Casino Royale  1967   David Niven     Ken Hughes       315.0   \n",
       "\n",
       "   Budget  Bond Actor Salary  \n",
       "0     7.0                0.6  \n",
       "1    12.6                1.6  \n",
       "2    18.6                3.2  \n",
       "3    41.9                4.7  \n",
       "4    85.0                NaN  "
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.reset_index().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3c9817c",
   "metadata": {},
   "source": [
    "### <font color=blue> Retrieve rows by index label with `loc []` Accessor </font>\n",
    "\n",
    "* `loc` is a pandas's property.\n",
    "* **label-based selection**\n",
    "* Similar to list slicing except in slicing, range stop value is exclusive whereas `loc`'s stop value is inclusive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "c9b97ebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Actor</th>\n",
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       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>Dr. No</th>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year         Actor       Director  Box Office  Budget  \\\n",
       "Film                                                                           \n",
       "Dr. No                 1962  Sean Connery  Terence Young       448.8     7.0   \n",
       "From Russia with Love  1963  Sean Connery  Terence Young       543.8    12.6   \n",
       "Goldfinger             1964  Sean Connery   Guy Hamilton       820.4    18.6   \n",
       "Thunderball            1965  Sean Connery  Terence Young       848.1    41.9   \n",
       "Casino Royale          1967   David Niven     Ken Hughes       315.0    85.0   \n",
       "\n",
       "                       Bond Actor Salary  \n",
       "Film                                      \n",
       "Dr. No                               0.6  \n",
       "From Russia with Love                1.6  \n",
       "Goldfinger                           3.2  \n",
       "Thunderball                          4.7  \n",
       "Casino Royale                        NaN  "
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond = pd.read_csv(\"./datasets/jamesbond.csv\", index_col = \"Film\")\n",
    "bond.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "1ea931fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year                          1965\n",
       "Actor                 Sean Connery\n",
       "Director             Terence Young\n",
       "Box Office                   848.1\n",
       "Budget                        41.9\n",
       "Bond Actor Salary              4.7\n",
       "Name: Thunderball, dtype: object"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mind the square bracket\n",
    "\n",
    "bond.loc[\"Thunderball\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "12246fdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>2006</td>\n",
       "      <td>Daniel Craig</td>\n",
       "      <td>Martin Campbell</td>\n",
       "      <td>581.5</td>\n",
       "      <td>145.3</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Year         Actor         Director  Box Office  Budget  \\\n",
       "Film                                                                     \n",
       "Casino Royale  1967   David Niven       Ken Hughes       315.0    85.0   \n",
       "Casino Royale  2006  Daniel Craig  Martin Campbell       581.5   145.3   \n",
       "Dr. No         1962  Sean Connery    Terence Young       448.8     7.0   \n",
       "\n",
       "               Bond Actor Salary  \n",
       "Film                              \n",
       "Casino Royale                NaN  \n",
       "Casino Royale                3.3  \n",
       "Dr. No                       0.6  "
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.loc[ [\"Casino Royale\", \"Dr. No\"] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "afbed893",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GoldenEye</th>\n",
       "      <td>1995</td>\n",
       "      <td>Pierce Brosnan</td>\n",
       "      <td>Martin Campbell</td>\n",
       "      <td>518.5</td>\n",
       "      <td>76.9</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomorrow Never Dies</th>\n",
       "      <td>1997</td>\n",
       "      <td>Pierce Brosnan</td>\n",
       "      <td>Roger Spottiswoode</td>\n",
       "      <td>463.2</td>\n",
       "      <td>133.9</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The World Is Not Enough</th>\n",
       "      <td>1999</td>\n",
       "      <td>Pierce Brosnan</td>\n",
       "      <td>Michael Apted</td>\n",
       "      <td>439.5</td>\n",
       "      <td>158.3</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Die Another Day</th>\n",
       "      <td>2002</td>\n",
       "      <td>Pierce Brosnan</td>\n",
       "      <td>Lee Tamahori</td>\n",
       "      <td>465.4</td>\n",
       "      <td>154.2</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>2006</td>\n",
       "      <td>Daniel Craig</td>\n",
       "      <td>Martin Campbell</td>\n",
       "      <td>581.5</td>\n",
       "      <td>145.3</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantum of Solace</th>\n",
       "      <td>2008</td>\n",
       "      <td>Daniel Craig</td>\n",
       "      <td>Marc Forster</td>\n",
       "      <td>514.2</td>\n",
       "      <td>181.4</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skyfall</th>\n",
       "      <td>2012</td>\n",
       "      <td>Daniel Craig</td>\n",
       "      <td>Sam Mendes</td>\n",
       "      <td>943.5</td>\n",
       "      <td>170.2</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spectre</th>\n",
       "      <td>2015</td>\n",
       "      <td>Daniel Craig</td>\n",
       "      <td>Sam Mendes</td>\n",
       "      <td>726.7</td>\n",
       "      <td>206.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Year           Actor            Director  Box Office  \\\n",
       "Film                                                                            \n",
       "GoldenEye                1995  Pierce Brosnan     Martin Campbell       518.5   \n",
       "Tomorrow Never Dies      1997  Pierce Brosnan  Roger Spottiswoode       463.2   \n",
       "The World Is Not Enough  1999  Pierce Brosnan       Michael Apted       439.5   \n",
       "Die Another Day          2002  Pierce Brosnan        Lee Tamahori       465.4   \n",
       "Casino Royale            2006    Daniel Craig     Martin Campbell       581.5   \n",
       "Quantum of Solace        2008    Daniel Craig        Marc Forster       514.2   \n",
       "Skyfall                  2012    Daniel Craig          Sam Mendes       943.5   \n",
       "Spectre                  2015    Daniel Craig          Sam Mendes       726.7   \n",
       "\n",
       "                         Budget  Bond Actor Salary  \n",
       "Film                                                \n",
       "GoldenEye                  76.9                5.1  \n",
       "Tomorrow Never Dies       133.9               10.0  \n",
       "The World Is Not Enough   158.3               13.5  \n",
       "Die Another Day           154.2               17.9  \n",
       "Casino Royale             145.3                3.3  \n",
       "Quantum of Solace         181.4                8.1  \n",
       "Skyfall                   170.2               14.5  \n",
       "Spectre                   206.3                NaN  "
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.loc[\"GoldenEye\" :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "cb5a6711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GoldenEye</th>\n",
       "      <td>1995</td>\n",
       "      <td>Pierce Brosnan</td>\n",
       "      <td>Martin Campbell</td>\n",
       "      <td>518.5</td>\n",
       "      <td>76.9</td>\n",
       "      <td>5.1</td>\n",
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       "    <tr>\n",
       "      <th>Tomorrow Never Dies</th>\n",
       "      <td>1997</td>\n",
       "      <td>Pierce Brosnan</td>\n",
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       "      <td>Pierce Brosnan</td>\n",
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       "      <td>439.5</td>\n",
       "      <td>158.3</td>\n",
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       "      <td>154.2</td>\n",
       "      <td>17.9</td>\n",
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       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>2006</td>\n",
       "      <td>Daniel Craig</td>\n",
       "      <td>Martin Campbell</td>\n",
       "      <td>581.5</td>\n",
       "      <td>145.3</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantum of Solace</th>\n",
       "      <td>2008</td>\n",
       "      <td>Daniel Craig</td>\n",
       "      <td>Marc Forster</td>\n",
       "      <td>514.2</td>\n",
       "      <td>181.4</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skyfall</th>\n",
       "      <td>2012</td>\n",
       "      <td>Daniel Craig</td>\n",
       "      <td>Sam Mendes</td>\n",
       "      <td>943.5</td>\n",
       "      <td>170.2</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Year           Actor            Director  Box Office  \\\n",
       "Film                                                                            \n",
       "GoldenEye                1995  Pierce Brosnan     Martin Campbell       518.5   \n",
       "Tomorrow Never Dies      1997  Pierce Brosnan  Roger Spottiswoode       463.2   \n",
       "The World Is Not Enough  1999  Pierce Brosnan       Michael Apted       439.5   \n",
       "Die Another Day          2002  Pierce Brosnan        Lee Tamahori       465.4   \n",
       "Casino Royale            2006    Daniel Craig     Martin Campbell       581.5   \n",
       "Quantum of Solace        2008    Daniel Craig        Marc Forster       514.2   \n",
       "Skyfall                  2012    Daniel Craig          Sam Mendes       943.5   \n",
       "\n",
       "                         Budget  Bond Actor Salary  \n",
       "Film                                                \n",
       "GoldenEye                  76.9                5.1  \n",
       "Tomorrow Never Dies       133.9               10.0  \n",
       "The World Is Not Enough   158.3               13.5  \n",
       "Die Another Day           154.2               17.9  \n",
       "Casino Royale             145.3                3.3  \n",
       "Quantum of Solace         181.4                8.1  \n",
       "Skyfall                   170.2               14.5  "
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stop range inclusive\n",
    "\n",
    "bond.loc[\"GoldenEye\" : \"Skyfall\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "64d79a31",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>The World Is Not Enough</th>\n",
       "      <td>1999</td>\n",
       "      <td>Pierce Brosnan</td>\n",
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       "      <td>439.5</td>\n",
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       "      <th>Casino Royale</th>\n",
       "      <td>2006</td>\n",
       "      <td>Daniel Craig</td>\n",
       "      <td>Martin Campbell</td>\n",
       "      <td>581.5</td>\n",
       "      <td>145.3</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skyfall</th>\n",
       "      <td>2012</td>\n",
       "      <td>Daniel Craig</td>\n",
       "      <td>Sam Mendes</td>\n",
       "      <td>943.5</td>\n",
       "      <td>170.2</td>\n",
       "      <td>14.5</td>\n",
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      ],
      "text/plain": [
       "                         Year           Actor         Director  Box Office  \\\n",
       "Film                                                                         \n",
       "GoldenEye                1995  Pierce Brosnan  Martin Campbell       518.5   \n",
       "The World Is Not Enough  1999  Pierce Brosnan    Michael Apted       439.5   \n",
       "Casino Royale            2006    Daniel Craig  Martin Campbell       581.5   \n",
       "Skyfall                  2012    Daniel Craig       Sam Mendes       943.5   \n",
       "\n",
       "                         Budget  Bond Actor Salary  \n",
       "Film                                                \n",
       "GoldenEye                  76.9                5.1  \n",
       "The World Is Not Enough   158.3               13.5  \n",
       "Casino Royale             145.3                3.3  \n",
       "Skyfall                   170.2               14.5  "
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.loc[\"GoldenEye\" : \"Skyfall\" : 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "1bb4fa66",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Apple</th>\n",
       "      <th>Mango</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1</th>\n",
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      ],
      "text/plain": [
       "   Apple  Mango\n",
       "1    120     56\n",
       "2     55     78\n",
       "3     86     95"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_df = pd.DataFrame(data = {\"Apple\": [120, 55, 86], \"Mango\": [56, 78, 95]}, index = [1, 2, 3])\n",
    "sample_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "08f04da8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Apple</th>\n",
       "      <th>Mango</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Apple  Mango\n",
       "1    120     56\n",
       "3     86     95"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bond.loc[ [1, 3] ] -> gives error bcs it's index values are not integers but strings\n",
    "\n",
    "sample_df.loc[ [1, 3] ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a25a4a8",
   "metadata": {},
   "source": [
    "### <font color=blue> Retrieve rows by index position with `iloc []` Accessor </font>\n",
    "\n",
    "* **index-based selection**\n",
    "* Difference between iloc and loc:\n",
    "    1. loc gets rows (and/or columns) with particular labels. iloc gets rows (and/or columns) at integer locations.\n",
    "        ``` \n",
    "        bond.iloc[\"Skyfall\"] -> this will give error \n",
    "        ```\n",
    "    2. iloc method does not include the last element of the range passed in it unlike loc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "bc92f764",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year         Actor       Director  Box Office  Budget  \\\n",
       "Film                                                                           \n",
       "Dr. No                 1962  Sean Connery  Terence Young       448.8     7.0   \n",
       "From Russia with Love  1963  Sean Connery  Terence Young       543.8    12.6   \n",
       "Goldfinger             1964  Sean Connery   Guy Hamilton       820.4    18.6   \n",
       "Thunderball            1965  Sean Connery  Terence Young       848.1    41.9   \n",
       "Casino Royale          1967   David Niven     Ken Hughes       315.0    85.0   \n",
       "\n",
       "                       Bond Actor Salary  \n",
       "Film                                      \n",
       "Dr. No                               0.6  \n",
       "From Russia with Love                1.6  \n",
       "Goldfinger                           3.2  \n",
       "Thunderball                          4.7  \n",
       "Casino Royale                        NaN  "
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "86985d11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year         Actor       Director  Box Office  Budget  \\\n",
       "Film                                                                           \n",
       "From Russia with Love  1963  Sean Connery  Terence Young       543.8    12.6   \n",
       "Goldfinger             1964  Sean Connery   Guy Hamilton       820.4    18.6   \n",
       "\n",
       "                       Bond Actor Salary  \n",
       "Film                                      \n",
       "From Russia with Love                1.6  \n",
       "Goldfinger                           3.2  "
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stop range doesn't included\n",
    "\n",
    "bond.iloc[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "babf6e29",
   "metadata": {},
   "outputs": [],
   "source": [
    "bond.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "afab2a59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "      <th>Film</th>\n",
       "      <th>Year</th>\n",
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       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dr. No</td>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>From Russia with Love</td>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Goldfinger</td>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thunderball</td>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Casino Royale</td>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Film  Year         Actor       Director  Box Office  \\\n",
       "0                 Dr. No  1962  Sean Connery  Terence Young       448.8   \n",
       "1  From Russia with Love  1963  Sean Connery  Terence Young       543.8   \n",
       "2             Goldfinger  1964  Sean Connery   Guy Hamilton       820.4   \n",
       "3            Thunderball  1965  Sean Connery  Terence Young       848.1   \n",
       "4          Casino Royale  1967   David Niven     Ken Hughes       315.0   \n",
       "\n",
       "   Budget  Bond Actor Salary  \n",
       "0     7.0                0.6  \n",
       "1    12.6                1.6  \n",
       "2    18.6                3.2  \n",
       "3    41.9                4.7  \n",
       "4    85.0                NaN  "
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "499dbe87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>0</th>\n",
       "      <td>Dr. No</td>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thunderball</td>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>You Only Live Twice</td>\n",
       "      <td>1967</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Lewis Gilbert</td>\n",
       "      <td>514.2</td>\n",
       "      <td>59.9</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Film  Year         Actor       Director  Box Office  Budget  \\\n",
       "0               Dr. No  1962  Sean Connery  Terence Young       448.8     7.0   \n",
       "3          Thunderball  1965  Sean Connery  Terence Young       848.1    41.9   \n",
       "5  You Only Live Twice  1967  Sean Connery  Lewis Gilbert       514.2    59.9   \n",
       "\n",
       "   Bond Actor Salary  \n",
       "0                0.6  \n",
       "3                4.7  \n",
       "5                4.4  "
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.iloc[ [0, 3, 5] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "5c24e832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Film</th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dr. No</td>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>From Russia with Love</td>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Goldfinger</td>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Film  Year         Actor       Director  Box Office  \\\n",
       "0                 Dr. No  1962  Sean Connery  Terence Young       448.8   \n",
       "1  From Russia with Love  1963  Sean Connery  Terence Young       543.8   \n",
       "2             Goldfinger  1964  Sean Connery   Guy Hamilton       820.4   \n",
       "\n",
       "   Budget  Bond Actor Salary  \n",
       "0     7.0                0.6  \n",
       "1    12.6                1.6  \n",
       "2    18.6                3.2  "
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.iloc[ : 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "8be54a68",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Budget</th>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>The Spy Who Loved Me</td>\n",
       "      <td>1977</td>\n",
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       "      <td>533.0</td>\n",
       "      <td>45.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>The World Is Not Enough</td>\n",
       "      <td>1999</td>\n",
       "      <td>Pierce Brosnan</td>\n",
       "      <td>Michael Apted</td>\n",
       "      <td>439.5</td>\n",
       "      <td>158.3</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Film  Year           Actor       Director  Box Office  \\\n",
       "0                    Dr. No  1962    Sean Connery  Terence Young       448.8   \n",
       "10     The Spy Who Loved Me  1977     Roger Moore  Lewis Gilbert       533.0   \n",
       "20  The World Is Not Enough  1999  Pierce Brosnan  Michael Apted       439.5   \n",
       "\n",
       "    Budget  Bond Actor Salary  \n",
       "0      7.0                0.6  \n",
       "10    45.1                NaN  \n",
       "20   158.3               13.5  "
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.iloc[ : : 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90b095db",
   "metadata": {},
   "source": [
    "### <font color=blue>Passing second arguments to the loc and iloc Accessors</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "67d8323a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Film</th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dr. No</td>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>From Russia with Love</td>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Goldfinger</td>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thunderball</td>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Casino Royale</td>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Film  Year         Actor       Director  Box Office  \\\n",
       "0                 Dr. No  1962  Sean Connery  Terence Young       448.8   \n",
       "1  From Russia with Love  1963  Sean Connery  Terence Young       543.8   \n",
       "2             Goldfinger  1964  Sean Connery   Guy Hamilton       820.4   \n",
       "3            Thunderball  1965  Sean Connery  Terence Young       848.1   \n",
       "4          Casino Royale  1967   David Niven     Ken Hughes       315.0   \n",
       "\n",
       "   Budget  Bond Actor Salary  \n",
       "0     7.0                0.6  \n",
       "1    12.6                1.6  \n",
       "2    18.6                3.2  \n",
       "3    41.9                4.7  \n",
       "4    85.0                NaN  "
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "64f471e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year         Actor       Director  Box Office  Budget  \\\n",
       "Film                                                                           \n",
       "Dr. No                 1962  Sean Connery  Terence Young       448.8     7.0   \n",
       "From Russia with Love  1963  Sean Connery  Terence Young       543.8    12.6   \n",
       "Goldfinger             1964  Sean Connery   Guy Hamilton       820.4    18.6   \n",
       "Thunderball            1965  Sean Connery  Terence Young       848.1    41.9   \n",
       "Casino Royale          1967   David Niven     Ken Hughes       315.0    85.0   \n",
       "\n",
       "                       Bond Actor Salary  \n",
       "Film                                      \n",
       "Dr. No                               0.6  \n",
       "From Russia with Love                1.6  \n",
       "Goldfinger                           3.2  \n",
       "Thunderball                          4.7  \n",
       "Casino Royale                        NaN  "
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.set_index(\"Film\", inplace = True)\n",
    "bond.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "84df4de3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year                         1964\n",
       "Actor                Sean Connery\n",
       "Director             Guy Hamilton\n",
       "Box Office                  820.4\n",
       "Budget                       18.6\n",
       "Bond Actor Salary             3.2\n",
       "Name: Goldfinger, dtype: object"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.loc[\"Goldfinger\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "62c287ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sean Connery'"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first param is row index label and second param is col to extract\n",
    "\n",
    "bond.loc[\"Goldfinger\", \"Actor\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "ceddcefb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Box Office           820.4\n",
       "Actor         Sean Connery\n",
       "Budget                18.6\n",
       "Name: Goldfinger, dtype: object"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.loc[\"Goldfinger\", [\"Box Office\", \"Actor\", \"Budget\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "a8767654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Actor         Sean Connery\n",
       "Director      Guy Hamilton\n",
       "Box Office           820.4\n",
       "Name: Goldfinger, dtype: object"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.loc[\"Goldfinger\", \"Actor\":\"Box Office\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "5160120a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Quantum of Solace</th>\n",
       "      <td>2008</td>\n",
       "      <td>Daniel Craig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skyfall</th>\n",
       "      <td>2012</td>\n",
       "      <td>Daniel Craig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spectre</th>\n",
       "      <td>2015</td>\n",
       "      <td>Daniel Craig</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Year         Actor\n",
       "Film                                 \n",
       "Quantum of Solace  2008  Daniel Craig\n",
       "Skyfall            2012  Daniel Craig\n",
       "Spectre            2015  Daniel Craig"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.loc[\"Quantum of Solace\":, :\"Actor\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "42d304cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year         Actor       Director  Box Office  Budget  \\\n",
       "Film                                                                           \n",
       "Dr. No                 1962  Sean Connery  Terence Young       448.8     7.0   \n",
       "From Russia with Love  1963  Sean Connery  Terence Young       543.8    12.6   \n",
       "Goldfinger             1964  Sean Connery   Guy Hamilton       820.4    18.6   \n",
       "Thunderball            1965  Sean Connery  Terence Young       848.1    41.9   \n",
       "Casino Royale          1967   David Niven     Ken Hughes       315.0    85.0   \n",
       "\n",
       "                       Bond Actor Salary  \n",
       "Film                                      \n",
       "Dr. No                               0.6  \n",
       "From Russia with Love                1.6  \n",
       "Goldfinger                           3.2  \n",
       "Thunderball                          4.7  \n",
       "Casino Royale                        NaN  "
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "640b5e9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Director    Guy Hamilton\n",
       "Actor       Sean Connery\n",
       "Name: Goldfinger, dtype: object"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# same applies to iloc except pass index rather than labels or col name\n",
    "\n",
    "bond.iloc[2, [2, 1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "307058a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26, 6)"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "cf9a65eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year         Actor       Director  Box Office  Budget\n",
       "Film                                                                        \n",
       "Dr. No                 1962  Sean Connery  Terence Young       448.8     7.0\n",
       "From Russia with Love  1963  Sean Connery  Terence Young       543.8    12.6"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.iloc[:2, :-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ca2b2ea",
   "metadata": {},
   "source": [
    "### <font color=blue> Set New Value for a Specific Cell or Cells In a Row </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "ea60ea9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "bond.loc[\"Dr. No\", \"Actor\"] = \"Mr. Actor\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "7bf7fe31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>1962</td>\n",
       "      <td>Mr. Actor</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year         Actor       Director  Box Office  Budget  \\\n",
       "Film                                                                           \n",
       "Dr. No                 1962     Mr. Actor  Terence Young       448.8     7.0   \n",
       "From Russia with Love  1963  Sean Connery  Terence Young       543.8    12.6   \n",
       "Goldfinger             1964  Sean Connery   Guy Hamilton       820.4    18.6   \n",
       "Thunderball            1965  Sean Connery  Terence Young       848.1    41.9   \n",
       "Casino Royale          1967   David Niven     Ken Hughes       315.0    85.0   \n",
       "\n",
       "                       Bond Actor Salary  \n",
       "Film                                      \n",
       "Dr. No                               0.6  \n",
       "From Russia with Love                1.6  \n",
       "Goldfinger                           3.2  \n",
       "Thunderball                          4.7  \n",
       "Casino Royale                        NaN  "
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "5d578c7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "bond.loc[\"Dr. No\" : \"Goldfinger\", [\"Budget\", \"Year\"]] = [7, 2004]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "c4898f6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>2004</td>\n",
       "      <td>Mr. Actor</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>2004</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>2004</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year         Actor       Director  Box Office  Budget  \\\n",
       "Film                                                                           \n",
       "Dr. No                 2004     Mr. Actor  Terence Young       448.8     7.0   \n",
       "From Russia with Love  2004  Sean Connery  Terence Young       543.8     7.0   \n",
       "Goldfinger             2004  Sean Connery   Guy Hamilton       820.4     7.0   \n",
       "Thunderball            1965  Sean Connery  Terence Young       848.1    41.9   \n",
       "Casino Royale          1967   David Niven     Ken Hughes       315.0    85.0   \n",
       "\n",
       "                       Bond Actor Salary  \n",
       "Film                                      \n",
       "Dr. No                               0.6  \n",
       "From Russia with Love                1.6  \n",
       "Goldfinger                           3.2  \n",
       "Thunderball                          4.7  \n",
       "Casino Royale                        NaN  "
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "48a31012",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>2004</td>\n",
       "      <td>Mr. Actor</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>2005</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>2000</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year         Actor       Director  Box Office  Budget  \\\n",
       "Film                                                                           \n",
       "Dr. No                 2004     Mr. Actor  Terence Young       448.8    17.0   \n",
       "From Russia with Love  2005  Sean Connery  Terence Young       543.8     8.0   \n",
       "Goldfinger             2000  Sean Connery   Guy Hamilton       820.4     5.0   \n",
       "Thunderball            1965  Sean Connery  Terence Young       848.1    41.9   \n",
       "Casino Royale          1967   David Niven     Ken Hughes       315.0    85.0   \n",
       "\n",
       "                       Bond Actor Salary  \n",
       "Film                                      \n",
       "Dr. No                               0.6  \n",
       "From Russia with Love                1.6  \n",
       "Goldfinger                           3.2  \n",
       "Thunderball                          4.7  \n",
       "Casino Royale                        NaN  "
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.loc[\"Dr. No\" : \"Goldfinger\", [\"Budget\", \"Year\"]] = [[17, 2004], [8, 2005], [5, 2000]]\n",
    "bond.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f451f96",
   "metadata": {},
   "source": [
    "### <font color=blue> Set Multiple Values in a DataFrame </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "c0a94877",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Film\n",
       "Dr. No                             False\n",
       "From Russia with Love               True\n",
       "Goldfinger                          True\n",
       "Thunderball                         True\n",
       "Casino Royale                      False\n",
       "You Only Live Twice                 True\n",
       "On Her Majesty's Secret Service    False\n",
       "Diamonds Are Forever                True\n",
       "Live and Let Die                   False\n",
       "The Man with the Golden Gun        False\n",
       "The Spy Who Loved Me               False\n",
       "Moonraker                          False\n",
       "For Your Eyes Only                 False\n",
       "Never Say Never Again               True\n",
       "Octopussy                          False\n",
       "A View to a Kill                   False\n",
       "The Living Daylights               False\n",
       "Licence to Kill                    False\n",
       "GoldenEye                          False\n",
       "Tomorrow Never Dies                False\n",
       "The World Is Not Enough            False\n",
       "Die Another Day                    False\n",
       "Casino Royale                      False\n",
       "Quantum of Solace                  False\n",
       "Skyfall                            False\n",
       "Spectre                            False\n",
       "Name: Actor, dtype: bool"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# suppose we want to replace Actor col value Sean Connery to Sir Sean Connery whereever found\n",
    "\n",
    "bond[\"Actor\"] == \"Sean Connery\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "bc779839",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>2005</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>2000</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>You Only Live Twice</th>\n",
       "      <td>1967</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Lewis Gilbert</td>\n",
       "      <td>514.2</td>\n",
       "      <td>59.9</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diamonds Are Forever</th>\n",
       "      <td>1971</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>442.5</td>\n",
       "      <td>34.7</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never Say Never Again</th>\n",
       "      <td>1983</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Irvin Kershner</td>\n",
       "      <td>380.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year         Actor        Director  Box Office  Budget  \\\n",
       "Film                                                                            \n",
       "From Russia with Love  2005  Sean Connery   Terence Young       543.8     8.0   \n",
       "Goldfinger             2000  Sean Connery    Guy Hamilton       820.4     5.0   \n",
       "Thunderball            1965  Sean Connery   Terence Young       848.1    41.9   \n",
       "You Only Live Twice    1967  Sean Connery   Lewis Gilbert       514.2    59.9   \n",
       "Diamonds Are Forever   1971  Sean Connery    Guy Hamilton       442.5    34.7   \n",
       "Never Say Never Again  1983  Sean Connery  Irvin Kershner       380.0    86.0   \n",
       "\n",
       "                       Bond Actor Salary  \n",
       "Film                                      \n",
       "From Russia with Love                1.6  \n",
       "Goldfinger                           3.2  \n",
       "Thunderball                          4.7  \n",
       "You Only Live Twice                  4.4  \n",
       "Diamonds Are Forever                 5.8  \n",
       "Never Say Never Again                NaN  "
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool_ser_sean_connery = bond[\"Actor\"] == \"Sean Connery\"\n",
    "bond[bool_ser_sean_connery]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "7aabb1af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Film\n",
       "From Russia with Love    Sean Connery\n",
       "Goldfinger               Sean Connery\n",
       "Thunderball              Sean Connery\n",
       "You Only Live Twice      Sean Connery\n",
       "Diamonds Are Forever     Sean Connery\n",
       "Never Say Never Again    Sean Connery\n",
       "Name: Actor, dtype: object"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond[bool_ser_sean_connery][\"Actor\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "678bcabf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now this will give error as it's a copy not original\n",
    "# bond[bool_ser_sean_connery][\"Actor\"] = \"Sir Sean Connery\" # -> Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "d7f593d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>2004</td>\n",
       "      <td>Mr. Actor</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>2005</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>2000</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year             Actor       Director  Box Office  \\\n",
       "Film                                                                       \n",
       "Dr. No                 2004         Mr. Actor  Terence Young       448.8   \n",
       "From Russia with Love  2005  Sir Sean Connery  Terence Young       543.8   \n",
       "Goldfinger             2000  Sir Sean Connery   Guy Hamilton       820.4   \n",
       "Thunderball            1965  Sir Sean Connery  Terence Young       848.1   \n",
       "Casino Royale          1967       David Niven     Ken Hughes       315.0   \n",
       "\n",
       "                       Budget  Bond Actor Salary  \n",
       "Film                                              \n",
       "Dr. No                   17.0                0.6  \n",
       "From Russia with Love     8.0                1.6  \n",
       "Goldfinger                5.0                3.2  \n",
       "Thunderball              41.9                4.7  \n",
       "Casino Royale            85.0                NaN  "
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can also pass boolean series to loc[]\n",
    "\n",
    "bond.loc[bool_ser_sean_connery, \"Actor\"] = \"Sir Sean Connery\"\n",
    "bond.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a34eb4c",
   "metadata": {},
   "source": [
    "### <font color=blue> Rename Index Labels or Columns in a DataFrame </font>\n",
    "\n",
    "* This can be achieved by using `df.rename()` method.\n",
    "* Column renaming can also be done using `df.columns` attribute, but we need to assign the same col length list to it.\n",
    "* Column renaming with `df.columns` is an inplace operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "dcf4f836",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DrNo</th>\n",
       "      <td>2004</td>\n",
       "      <td>Mr. Actor</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>2005</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gold Finger</th>\n",
       "      <td>2000</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year             Actor       Director  Box Office  \\\n",
       "Film                                                                       \n",
       "DrNo                   2004         Mr. Actor  Terence Young       448.8   \n",
       "From Russia with Love  2005  Sir Sean Connery  Terence Young       543.8   \n",
       "Gold Finger            2000  Sir Sean Connery   Guy Hamilton       820.4   \n",
       "Thunderball            1965  Sir Sean Connery  Terence Young       848.1   \n",
       "Casino Royale          1967       David Niven     Ken Hughes       315.0   \n",
       "\n",
       "                       Budget  Bond Actor Salary  \n",
       "Film                                              \n",
       "DrNo                     17.0                0.6  \n",
       "From Russia with Love     8.0                1.6  \n",
       "Gold Finger               5.0                3.2  \n",
       "Thunderball              41.9                4.7  \n",
       "Casino Royale            85.0                NaN  "
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's rename index Film:Goldfinger to Gold Finger and Dr. No to DrNo\n",
    "\n",
    "bond.rename(mapper = {\n",
    "    \"Goldfinger\": \"Gold Finger\",\n",
    "    \"Dr. No\": \"DrNo\"\n",
    "}).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "9ae0d754",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DrNo</th>\n",
       "      <td>2004</td>\n",
       "      <td>Mr. Actor</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>2005</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gold Finger</th>\n",
       "      <td>2000</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year             Actor       Director  Box Office  \\\n",
       "Film                                                                       \n",
       "DrNo                   2004         Mr. Actor  Terence Young       448.8   \n",
       "From Russia with Love  2005  Sir Sean Connery  Terence Young       543.8   \n",
       "Gold Finger            2000  Sir Sean Connery   Guy Hamilton       820.4   \n",
       "Thunderball            1965  Sir Sean Connery  Terence Young       848.1   \n",
       "Casino Royale          1967       David Niven     Ken Hughes       315.0   \n",
       "\n",
       "                       Budget  Bond Actor Salary  \n",
       "Film                                              \n",
       "DrNo                     17.0                0.6  \n",
       "From Russia with Love     8.0                1.6  \n",
       "Gold Finger               5.0                3.2  \n",
       "Thunderball              41.9                4.7  \n",
       "Casino Royale            85.0                NaN  "
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# above code can be done like that also\n",
    "\n",
    "bond.rename(mapper = {\n",
    "    \"Goldfinger\": \"Gold Finger\",\n",
    "    \"Dr. No\": \"DrNo\"\n",
    "}, axis = 0)\n",
    "\n",
    "bond.rename(mapper = {\n",
    "    \"Goldfinger\": \"Gold Finger\",\n",
    "    \"Dr. No\": \"DrNo\"\n",
    "}, axis = \"index\")\n",
    "\n",
    "bond.rename(index = {\n",
    "    \"Goldfinger\": \"Gold Finger\",\n",
    "    \"Dr. No\": \"DrNo\"\n",
    "}).head()  # while specifying index as param, don't specify axis param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "236ab86d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>2004</td>\n",
       "      <td>Mr. Actor</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>2005</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>2000</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year             Actor       Director  Box Office  \\\n",
       "Film                                                                       \n",
       "Dr. No                 2004         Mr. Actor  Terence Young       448.8   \n",
       "From Russia with Love  2005  Sir Sean Connery  Terence Young       543.8   \n",
       "Goldfinger             2000  Sir Sean Connery   Guy Hamilton       820.4   \n",
       "Thunderball            1965  Sir Sean Connery  Terence Young       848.1   \n",
       "Casino Royale          1967       David Niven     Ken Hughes       315.0   \n",
       "\n",
       "                       Budget  Salary  \n",
       "Film                                   \n",
       "Dr. No                   17.0     0.6  \n",
       "From Russia with Love     8.0     1.6  \n",
       "Goldfinger                5.0     3.2  \n",
       "Thunderball              41.9     4.7  \n",
       "Casino Royale            85.0     NaN  "
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can also rename column name\n",
    "\n",
    "bond.rename(columns = {\"Bond Actor Salary\" : \"Salary\"}).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "cf4588d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Year', 'Actor', 'Director', 'Box Office', 'Budget',\n",
       "       'Bond Actor Salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# another way to rename column is using df.columns attribute\n",
    "\n",
    "bond.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "6d1cc8fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Released</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>2004</td>\n",
       "      <td>Mr. Actor</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>2005</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>2000</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Released             Actor       Director  Box Office  \\\n",
       "Film                                                                           \n",
       "Dr. No                     2004         Mr. Actor  Terence Young       448.8   \n",
       "From Russia with Love      2005  Sir Sean Connery  Terence Young       543.8   \n",
       "Goldfinger                 2000  Sir Sean Connery   Guy Hamilton       820.4   \n",
       "Thunderball                1965  Sir Sean Connery  Terence Young       848.1   \n",
       "Casino Royale              1967       David Niven     Ken Hughes       315.0   \n",
       "\n",
       "                       Cost  Salary  \n",
       "Film                                 \n",
       "Dr. No                 17.0     0.6  \n",
       "From Russia with Love   8.0     1.6  \n",
       "Goldfinger              5.0     3.2  \n",
       "Thunderball            41.9     4.7  \n",
       "Casino Royale          85.0     NaN  "
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and this operation is inplace\n",
    "\n",
    "bond.columns = ['Released', 'Actor', 'Director', 'Box Office', 'Cost', 'Salary']\n",
    "bond.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "501dfd9b",
   "metadata": {},
   "source": [
    "### <font color=blue> Delete Rows or Columns from a DataFrame </font>\n",
    "\n",
    "* This can be done using either:\n",
    "    1. `df.drop()`\n",
    "    2. `df.pop()`: inplace operation, columns deletion\n",
    "    3. `del df[\"Col_to_remove\"]`: inplace operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "56bb1989",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Released</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>2005</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>2000</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Released             Actor       Director  Box Office  \\\n",
       "Film                                                                           \n",
       "From Russia with Love      2005  Sir Sean Connery  Terence Young       543.8   \n",
       "Goldfinger                 2000  Sir Sean Connery   Guy Hamilton       820.4   \n",
       "Thunderball                1965  Sir Sean Connery  Terence Young       848.1   \n",
       "\n",
       "                       Cost  Salary  \n",
       "Film                                 \n",
       "From Russia with Love   8.0     1.6  \n",
       "Goldfinger              5.0     3.2  \n",
       "Thunderball            41.9     4.7  "
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop a row using index label\n",
    "\n",
    "bond.drop(\"Dr. No\").head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "20fda17e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Released</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>2005</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>2000</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Released             Actor       Director  Box Office  \\\n",
       "Film                                                                           \n",
       "From Russia with Love      2005  Sir Sean Connery  Terence Young       543.8   \n",
       "Goldfinger                 2000  Sir Sean Connery   Guy Hamilton       820.4   \n",
       "Thunderball                1965  Sir Sean Connery  Terence Young       848.1   \n",
       "\n",
       "                       Cost  Salary  \n",
       "Film                                 \n",
       "From Russia with Love   8.0     1.6  \n",
       "Goldfinger              5.0     3.2  \n",
       "Thunderball            41.9     4.7  "
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.drop(\"Dr. No\", axis = \"index\").head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "aecd98ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Released</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>2005</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>2000</td>\n",
       "      <td>Sir Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Released             Actor       Director  Box Office  \\\n",
       "Film                                                                           \n",
       "From Russia with Love      2005  Sir Sean Connery  Terence Young       543.8   \n",
       "Goldfinger                 2000  Sir Sean Connery   Guy Hamilton       820.4   \n",
       "\n",
       "                       Cost  Salary  \n",
       "Film                                 \n",
       "From Russia with Love   8.0     1.6  \n",
       "Goldfinger              5.0     3.2  "
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.drop(index = \"Dr. No\").head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "eebd8502",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Released</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>2004</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>2005</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>2000</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Released       Director  Box Office  Cost  Salary\n",
       "Film                                                                    \n",
       "Dr. No                     2004  Terence Young       448.8  17.0     0.6\n",
       "From Russia with Love      2005  Terence Young       543.8   8.0     1.6\n",
       "Goldfinger                 2000   Guy Hamilton       820.4   5.0     3.2"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping a column\n",
    "\n",
    "bond.drop(\"Actor\", axis = \"columns\").head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "6000d581",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Director  Box Office  Cost  Salary\n",
       "Film                                                          \n",
       "Dr. No                 Terence Young       448.8  17.0     0.6\n",
       "From Russia with Love  Terence Young       543.8   8.0     1.6\n",
       "Goldfinger              Guy Hamilton       820.4   5.0     3.2"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# above operation can be done like that also\n",
    "\n",
    "bond.drop(columns = [\"Actor\", \"Released\"]).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "bacbf9b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Film\n",
       "Dr. No                                    Mr. Actor\n",
       "From Russia with Love              Sir Sean Connery\n",
       "Goldfinger                         Sir Sean Connery\n",
       "Thunderball                        Sir Sean Connery\n",
       "Casino Royale                           David Niven\n",
       "You Only Live Twice                Sir Sean Connery\n",
       "On Her Majesty's Secret Service      George Lazenby\n",
       "Diamonds Are Forever               Sir Sean Connery\n",
       "Live and Let Die                        Roger Moore\n",
       "The Man with the Golden Gun             Roger Moore\n",
       "The Spy Who Loved Me                    Roger Moore\n",
       "Moonraker                               Roger Moore\n",
       "For Your Eyes Only                      Roger Moore\n",
       "Never Say Never Again              Sir Sean Connery\n",
       "Octopussy                               Roger Moore\n",
       "A View to a Kill                        Roger Moore\n",
       "The Living Daylights                 Timothy Dalton\n",
       "Licence to Kill                      Timothy Dalton\n",
       "GoldenEye                            Pierce Brosnan\n",
       "Tomorrow Never Dies                  Pierce Brosnan\n",
       "The World Is Not Enough              Pierce Brosnan\n",
       "Die Another Day                      Pierce Brosnan\n",
       "Casino Royale                          Daniel Craig\n",
       "Quantum of Solace                      Daniel Craig\n",
       "Skyfall                                Daniel Craig\n",
       "Spectre                                Daniel Craig\n",
       "Name: Actor, dtype: object"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pop() method perform inplace operation and also return the value\n",
    "\n",
    "actor = bond.pop(\"Actor\")\n",
    "actor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "751bb23e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Cost</th>\n",
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       "      <td>17.0</td>\n",
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       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>2005</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>2000</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Released       Director  Box Office  Cost  Salary\n",
       "Film                                                                    \n",
       "Dr. No                     2004  Terence Young       448.8  17.0     0.6\n",
       "From Russia with Love      2005  Terence Young       543.8   8.0     1.6\n",
       "Goldfinger                 2000   Guy Hamilton       820.4   5.0     3.2\n",
       "Thunderball                1965  Terence Young       848.1  41.9     4.7\n",
       "Casino Royale              1967     Ken Hughes       315.0  85.0     NaN"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "39e367dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "del bond[\"Cost\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "345c8a6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>2004</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>2005</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Released       Director  Box Office  Salary\n",
       "Film                                                              \n",
       "Dr. No                     2004  Terence Young       448.8     0.6\n",
       "From Russia with Love      2005  Terence Young       543.8     1.6"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf8b856",
   "metadata": {},
   "source": [
    "### <font color= blue> Create Random Sample with the sample Method </font>\n",
    "\n",
    "* Return a random sample of items from an axis of object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "282b2eff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Released</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>On Her Majesty's Secret Service</th>\n",
       "      <td>1969</td>\n",
       "      <td>Peter R. Hunt</td>\n",
       "      <td>291.5</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Released       Director  Box Office  Salary\n",
       "Film                                                                        \n",
       "On Her Majesty's Secret Service      1969  Peter R. Hunt       291.5     0.6"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "0df83cfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Released</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moonraker</th>\n",
       "      <td>1979</td>\n",
       "      <td>Lewis Gilbert</td>\n",
       "      <td>535.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>2006</td>\n",
       "      <td>Martin Campbell</td>\n",
       "      <td>581.5</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Released         Director  Box Office  Salary\n",
       "Film                                                        \n",
       "Casino Royale      1967       Ken Hughes       315.0     NaN\n",
       "Moonraker          1979    Lewis Gilbert       535.0     NaN\n",
       "Casino Royale      2006  Martin Campbell       581.5     3.3"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "de3cebe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Released</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>2000</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Spy Who Loved Me</th>\n",
       "      <td>1977</td>\n",
       "      <td>Lewis Gilbert</td>\n",
       "      <td>533.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Live and Let Die</th>\n",
       "      <td>1973</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>460.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Living Daylights</th>\n",
       "      <td>1987</td>\n",
       "      <td>John Glen</td>\n",
       "      <td>313.5</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diamonds Are Forever</th>\n",
       "      <td>1971</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>442.5</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A View to a Kill</th>\n",
       "      <td>1985</td>\n",
       "      <td>John Glen</td>\n",
       "      <td>275.2</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Released       Director  Box Office  Salary\n",
       "Film                                                             \n",
       "Goldfinger                2000   Guy Hamilton       820.4     3.2\n",
       "The Spy Who Loved Me      1977  Lewis Gilbert       533.0     NaN\n",
       "Live and Let Die          1973   Guy Hamilton       460.3     NaN\n",
       "The Living Daylights      1987      John Glen       313.5     5.2\n",
       "Diamonds Are Forever      1971   Guy Hamilton       442.5     5.8\n",
       "A View to a Kill          1985      John Glen       275.2     9.1"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.sample(frac = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "64485250",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Director</th>\n",
       "      <th>Released</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>Terence Young</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>Terence Young</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>Terence Young</td>\n",
       "      <td>1965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>1967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Director  Released\n",
       "Film                                          \n",
       "Dr. No                 Terence Young      2004\n",
       "From Russia with Love  Terence Young      2005\n",
       "Goldfinger              Guy Hamilton      2000\n",
       "Thunderball            Terence Young      1965\n",
       "Casino Royale             Ken Hughes      1967"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.sample(n = 2, axis = \"columns\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a718c9",
   "metadata": {},
   "source": [
    "### <font color=blue> Use the nsmallest / nlargest methods to get rows with smallest / largest values </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "52868936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Released</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>2004</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>2005</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Released       Director  Box Office  Salary\n",
       "Film                                                              \n",
       "Dr. No                     2004  Terence Young       448.8     0.6\n",
       "From Russia with Love      2005  Terence Young       543.8     1.6"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "daec3109",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Released</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Licence to Kill</th>\n",
       "      <td>1989</td>\n",
       "      <td>John Glen</td>\n",
       "      <td>250.9</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A View to a Kill</th>\n",
       "      <td>1985</td>\n",
       "      <td>John Glen</td>\n",
       "      <td>275.2</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>On Her Majesty's Secret Service</th>\n",
       "      <td>1969</td>\n",
       "      <td>Peter R. Hunt</td>\n",
       "      <td>291.5</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Released       Director  Box Office  Salary\n",
       "Film                                                                        \n",
       "Licence to Kill                      1989      John Glen       250.9     7.9\n",
       "A View to a Kill                     1985      John Glen       275.2     9.1\n",
       "On Her Majesty's Secret Service      1969  Peter R. Hunt       291.5     0.6"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.nsmallest(n = 3, columns = [\"Box Office\", \"Salary\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "0bccd239",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Released</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Die Another Day</th>\n",
       "      <td>2002</td>\n",
       "      <td>Lee Tamahori</td>\n",
       "      <td>465.4</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skyfall</th>\n",
       "      <td>2012</td>\n",
       "      <td>Sam Mendes</td>\n",
       "      <td>943.5</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Released      Director  Box Office  Salary\n",
       "Film                                                       \n",
       "Die Another Day      2002  Lee Tamahori       465.4    17.9\n",
       "Skyfall              2012    Sam Mendes       943.5    14.5"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.nlargest(n = 2, columns = \"Salary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "c7256e44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Film\n",
       "Skyfall        943.5\n",
       "Thunderball    848.1\n",
       "Goldfinger     820.4\n",
       "Name: Box Office, dtype: float64"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond[\"Box Office\"].nlargest(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fe19737",
   "metadata": {},
   "source": [
    "### <font color=blue> Filter A DataFrame with the where method </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "fa4d9ef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year         Actor       Director  Box Office  Budget  \\\n",
       "Film                                                                           \n",
       "Dr. No                 1962  Sean Connery  Terence Young       448.8     7.0   \n",
       "From Russia with Love  1963  Sean Connery  Terence Young       543.8    12.6   \n",
       "Goldfinger             1964  Sean Connery   Guy Hamilton       820.4    18.6   \n",
       "Thunderball            1965  Sean Connery  Terence Young       848.1    41.9   \n",
       "Casino Royale          1967   David Niven     Ken Hughes       315.0    85.0   \n",
       "\n",
       "                       Bond Actor Salary  \n",
       "Film                                      \n",
       "Dr. No                               0.6  \n",
       "From Russia with Love                1.6  \n",
       "Goldfinger                           3.2  \n",
       "Thunderball                          4.7  \n",
       "Casino Royale                        NaN  "
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond = pd.read_csv(\"./datasets/jamesbond.csv\", index_col = \"Film\")\n",
    "bond.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "a3380a67",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>You Only Live Twice</th>\n",
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       "      <td>514.2</td>\n",
       "      <td>59.9</td>\n",
       "      <td>4.4</td>\n",
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       "    <tr>\n",
       "      <th>Diamonds Are Forever</th>\n",
       "      <td>1971</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>442.5</td>\n",
       "      <td>34.7</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never Say Never Again</th>\n",
       "      <td>1983</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Irvin Kershner</td>\n",
       "      <td>380.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                       Year         Actor        Director  Box Office  Budget  \\\n",
       "Film                                                                            \n",
       "Dr. No                 1962  Sean Connery   Terence Young       448.8     7.0   \n",
       "From Russia with Love  1963  Sean Connery   Terence Young       543.8    12.6   \n",
       "Goldfinger             1964  Sean Connery    Guy Hamilton       820.4    18.6   \n",
       "Thunderball            1965  Sean Connery   Terence Young       848.1    41.9   \n",
       "You Only Live Twice    1967  Sean Connery   Lewis Gilbert       514.2    59.9   \n",
       "Diamonds Are Forever   1971  Sean Connery    Guy Hamilton       442.5    34.7   \n",
       "Never Say Never Again  1983  Sean Connery  Irvin Kershner       380.0    86.0   \n",
       "\n",
       "                       Bond Actor Salary  \n",
       "Film                                      \n",
       "Dr. No                               0.6  \n",
       "From Russia with Love                1.6  \n",
       "Goldfinger                           3.2  \n",
       "Thunderball                          4.7  \n",
       "You Only Live Twice                  4.4  \n",
       "Diamonds Are Forever                 5.8  \n",
       "Never Say Never Again                NaN  "
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask1 = bond[\"Actor\"] == \"Sean Connery\"\n",
    "bond[mask1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "85241e00",
   "metadata": {},
   "outputs": [
    {
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       "      <th>On Her Majesty's Secret Service</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>Moonraker</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Year         Actor        Director  \\\n",
       "Film                                                                    \n",
       "Dr. No                           1962.0  Sean Connery   Terence Young   \n",
       "From Russia with Love            1963.0  Sean Connery   Terence Young   \n",
       "Goldfinger                       1964.0  Sean Connery    Guy Hamilton   \n",
       "Thunderball                      1965.0  Sean Connery   Terence Young   \n",
       "Casino Royale                       NaN           NaN             NaN   \n",
       "You Only Live Twice              1967.0  Sean Connery   Lewis Gilbert   \n",
       "On Her Majesty's Secret Service     NaN           NaN             NaN   \n",
       "Diamonds Are Forever             1971.0  Sean Connery    Guy Hamilton   \n",
       "Live and Let Die                    NaN           NaN             NaN   \n",
       "The Man with the Golden Gun         NaN           NaN             NaN   \n",
       "The Spy Who Loved Me                NaN           NaN             NaN   \n",
       "Moonraker                           NaN           NaN             NaN   \n",
       "For Your Eyes Only                  NaN           NaN             NaN   \n",
       "Never Say Never Again            1983.0  Sean Connery  Irvin Kershner   \n",
       "Octopussy                           NaN           NaN             NaN   \n",
       "A View to a Kill                    NaN           NaN             NaN   \n",
       "The Living Daylights                NaN           NaN             NaN   \n",
       "Licence to Kill                     NaN           NaN             NaN   \n",
       "GoldenEye                           NaN           NaN             NaN   \n",
       "Tomorrow Never Dies                 NaN           NaN             NaN   \n",
       "The World Is Not Enough             NaN           NaN             NaN   \n",
       "Die Another Day                     NaN           NaN             NaN   \n",
       "Casino Royale                       NaN           NaN             NaN   \n",
       "Quantum of Solace                   NaN           NaN             NaN   \n",
       "Skyfall                             NaN           NaN             NaN   \n",
       "Spectre                             NaN           NaN             NaN   \n",
       "\n",
       "                                 Box Office  Budget  Bond Actor Salary  \n",
       "Film                                                                    \n",
       "Dr. No                                448.8     7.0                0.6  \n",
       "From Russia with Love                 543.8    12.6                1.6  \n",
       "Goldfinger                            820.4    18.6                3.2  \n",
       "Thunderball                           848.1    41.9                4.7  \n",
       "Casino Royale                           NaN     NaN                NaN  \n",
       "You Only Live Twice                   514.2    59.9                4.4  \n",
       "On Her Majesty's Secret Service         NaN     NaN                NaN  \n",
       "Diamonds Are Forever                  442.5    34.7                5.8  \n",
       "Live and Let Die                        NaN     NaN                NaN  \n",
       "The Man with the Golden Gun             NaN     NaN                NaN  \n",
       "The Spy Who Loved Me                    NaN     NaN                NaN  \n",
       "Moonraker                               NaN     NaN                NaN  \n",
       "For Your Eyes Only                      NaN     NaN                NaN  \n",
       "Never Say Never Again                 380.0    86.0                NaN  \n",
       "Octopussy                               NaN     NaN                NaN  \n",
       "A View to a Kill                        NaN     NaN                NaN  \n",
       "The Living Daylights                    NaN     NaN                NaN  \n",
       "Licence to Kill                         NaN     NaN                NaN  \n",
       "GoldenEye                               NaN     NaN                NaN  \n",
       "Tomorrow Never Dies                     NaN     NaN                NaN  \n",
       "The World Is Not Enough                 NaN     NaN                NaN  \n",
       "Die Another Day                         NaN     NaN                NaN  \n",
       "Casino Royale                           NaN     NaN                NaN  \n",
       "Quantum of Solace                       NaN     NaN                NaN  \n",
       "Skyfall                                 NaN     NaN                NaN  \n",
       "Spectre                                 NaN     NaN                NaN  "
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.where(mask1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "c4788757",
   "metadata": {},
   "outputs": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>1964.0</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965.0</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>You Only Live Twice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>On Her Majesty's Secret Service</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diamonds Are Forever</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Live and Let Die</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Man with the Golden Gun</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Spy Who Loved Me</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moonraker</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>For Your Eyes Only</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never Say Never Again</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Octopussy</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A View to a Kill</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Living Daylights</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Licence to Kill</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GoldenEye</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomorrow Never Dies</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The World Is Not Enough</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Die Another Day</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantum of Solace</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skyfall</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spectre</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Year         Actor       Director  \\\n",
       "Film                                                                   \n",
       "Dr. No                              NaN           NaN            NaN   \n",
       "From Russia with Love               NaN           NaN            NaN   \n",
       "Goldfinger                       1964.0  Sean Connery   Guy Hamilton   \n",
       "Thunderball                      1965.0  Sean Connery  Terence Young   \n",
       "Casino Royale                       NaN           NaN            NaN   \n",
       "You Only Live Twice                 NaN           NaN            NaN   \n",
       "On Her Majesty's Secret Service     NaN           NaN            NaN   \n",
       "Diamonds Are Forever                NaN           NaN            NaN   \n",
       "Live and Let Die                    NaN           NaN            NaN   \n",
       "The Man with the Golden Gun         NaN           NaN            NaN   \n",
       "The Spy Who Loved Me                NaN           NaN            NaN   \n",
       "Moonraker                           NaN           NaN            NaN   \n",
       "For Your Eyes Only                  NaN           NaN            NaN   \n",
       "Never Say Never Again               NaN           NaN            NaN   \n",
       "Octopussy                           NaN           NaN            NaN   \n",
       "A View to a Kill                    NaN           NaN            NaN   \n",
       "The Living Daylights                NaN           NaN            NaN   \n",
       "Licence to Kill                     NaN           NaN            NaN   \n",
       "GoldenEye                           NaN           NaN            NaN   \n",
       "Tomorrow Never Dies                 NaN           NaN            NaN   \n",
       "The World Is Not Enough             NaN           NaN            NaN   \n",
       "Die Another Day                     NaN           NaN            NaN   \n",
       "Casino Royale                       NaN           NaN            NaN   \n",
       "Quantum of Solace                   NaN           NaN            NaN   \n",
       "Skyfall                             NaN           NaN            NaN   \n",
       "Spectre                             NaN           NaN            NaN   \n",
       "\n",
       "                                 Box Office  Budget  Bond Actor Salary  \n",
       "Film                                                                    \n",
       "Dr. No                                  NaN     NaN                NaN  \n",
       "From Russia with Love                   NaN     NaN                NaN  \n",
       "Goldfinger                            820.4    18.6                3.2  \n",
       "Thunderball                           848.1    41.9                4.7  \n",
       "Casino Royale                           NaN     NaN                NaN  \n",
       "You Only Live Twice                     NaN     NaN                NaN  \n",
       "On Her Majesty's Secret Service         NaN     NaN                NaN  \n",
       "Diamonds Are Forever                    NaN     NaN                NaN  \n",
       "Live and Let Die                        NaN     NaN                NaN  \n",
       "The Man with the Golden Gun             NaN     NaN                NaN  \n",
       "The Spy Who Loved Me                    NaN     NaN                NaN  \n",
       "Moonraker                               NaN     NaN                NaN  \n",
       "For Your Eyes Only                      NaN     NaN                NaN  \n",
       "Never Say Never Again                   NaN     NaN                NaN  \n",
       "Octopussy                               NaN     NaN                NaN  \n",
       "A View to a Kill                        NaN     NaN                NaN  \n",
       "The Living Daylights                    NaN     NaN                NaN  \n",
       "Licence to Kill                         NaN     NaN                NaN  \n",
       "GoldenEye                               NaN     NaN                NaN  \n",
       "Tomorrow Never Dies                     NaN     NaN                NaN  \n",
       "The World Is Not Enough                 NaN     NaN                NaN  \n",
       "Die Another Day                         NaN     NaN                NaN  \n",
       "Casino Royale                           NaN     NaN                NaN  \n",
       "Quantum of Solace                       NaN     NaN                NaN  \n",
       "Skyfall                                 NaN     NaN                NaN  \n",
       "Spectre                                 NaN     NaN                NaN  "
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask2 = bond[\"Box Office\"] > 800\n",
    "bond.where(mask1 & mask2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e38e6190",
   "metadata": {},
   "source": [
    "### <font color=blue> Filter A DataFrame with the query method </font>\n",
    "\n",
    "* Before filtering df using query, first remve any space if there in column name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "97588094",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Year         Actor       Director  Box Office  Budget  \\\n",
       "Film                                                            \n",
       "Dr. No  1962  Sean Connery  Terence Young       448.8     7.0   \n",
       "\n",
       "        Bond Actor Salary  \n",
       "Film                       \n",
       "Dr. No                0.6  "
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "2e5fe16d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Year', 'Actor', 'Director', 'Box_Office', 'Budget', 'Bond_Actor_Salary']"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[\"_\".join(col_name.strip().split()) for col_name in bond.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "1650df09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box_Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond_Actor_Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Year         Actor       Director  Box_Office  Budget  \\\n",
       "Film                                                            \n",
       "Dr. No  1962  Sean Connery  Terence Young       448.8     7.0   \n",
       "\n",
       "        Bond_Actor_Salary  \n",
       "Film                       \n",
       "Dr. No                0.6  "
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.columns = [\"_\".join(col_name.strip().split()) for col_name in bond.columns]\n",
    "bond.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "e2aef8f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box_Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond_Actor_Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>You Only Live Twice</th>\n",
       "      <td>1967</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Lewis Gilbert</td>\n",
       "      <td>514.2</td>\n",
       "      <td>59.9</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diamonds Are Forever</th>\n",
       "      <td>1971</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>442.5</td>\n",
       "      <td>34.7</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never Say Never Again</th>\n",
       "      <td>1983</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Irvin Kershner</td>\n",
       "      <td>380.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year         Actor        Director  Box_Office  Budget  \\\n",
       "Film                                                                            \n",
       "Dr. No                 1962  Sean Connery   Terence Young       448.8     7.0   \n",
       "From Russia with Love  1963  Sean Connery   Terence Young       543.8    12.6   \n",
       "Goldfinger             1964  Sean Connery    Guy Hamilton       820.4    18.6   \n",
       "Thunderball            1965  Sean Connery   Terence Young       848.1    41.9   \n",
       "You Only Live Twice    1967  Sean Connery   Lewis Gilbert       514.2    59.9   \n",
       "Diamonds Are Forever   1971  Sean Connery    Guy Hamilton       442.5    34.7   \n",
       "Never Say Never Again  1983  Sean Connery  Irvin Kershner       380.0    86.0   \n",
       "\n",
       "                       Bond_Actor_Salary  \n",
       "Film                                      \n",
       "Dr. No                               0.6  \n",
       "From Russia with Love                1.6  \n",
       "Goldfinger                           3.2  \n",
       "Thunderball                          4.7  \n",
       "You Only Live Twice                  4.4  \n",
       "Diamonds Are Forever                 5.8  \n",
       "Never Say Never Again                NaN  "
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# queries can be written in Python syntax\n",
    "\n",
    "bond.query(\" Actor == 'Sean Connery' \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "94db6f53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box_Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond_Actor_Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Year         Actor       Director  Box_Office  Budget  \\\n",
       "Film                                                                 \n",
       "Goldfinger   1964  Sean Connery   Guy Hamilton       820.4    18.6   \n",
       "Thunderball  1965  Sean Connery  Terence Young       848.1    41.9   \n",
       "\n",
       "             Bond_Actor_Salary  \n",
       "Film                            \n",
       "Goldfinger                 3.2  \n",
       "Thunderball                4.7  "
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond.query(\" Actor == '   Sean Connery '.strip() and Box_Office > 800\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40b2150f",
   "metadata": {},
   "source": [
    "### <font color=blue> Create a Copy of a DataFrame with the copy Method </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "08dbe642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy Hamilton</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thunderball</th>\n",
       "      <td>1965</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>848.1</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casino Royale</th>\n",
       "      <td>1967</td>\n",
       "      <td>David Niven</td>\n",
       "      <td>Ken Hughes</td>\n",
       "      <td>315.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year         Actor       Director  Box Office  Budget  \\\n",
       "Film                                                                           \n",
       "Dr. No                 1962  Sean Connery  Terence Young       448.8     7.0   \n",
       "From Russia with Love  1963  Sean Connery  Terence Young       543.8    12.6   \n",
       "Goldfinger             1964  Sean Connery   Guy Hamilton       820.4    18.6   \n",
       "Thunderball            1965  Sean Connery  Terence Young       848.1    41.9   \n",
       "Casino Royale          1967   David Niven     Ken Hughes       315.0    85.0   \n",
       "\n",
       "                       Bond Actor Salary  \n",
       "Film                                      \n",
       "Dr. No                               0.6  \n",
       "From Russia with Love                1.6  \n",
       "Goldfinger                           3.2  \n",
       "Thunderball                          4.7  \n",
       "Casino Royale                        NaN  "
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond = pd.read_csv(\"./datasets/jamesbond.csv\", index_col = \"Film\")\n",
    "bond.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "83f31c66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Film\n",
       "Dr. No                   Terence Young\n",
       "From Russia with Love    Terence Young\n",
       "Goldfinger                Guy Hamilton\n",
       "Name: Director, dtype: object"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directors = bond[\"Director\"]\n",
    "directors.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "9cf3c0e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Guy Hamilton'"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now if change something in directors variable, it will be reflected in the original df\n",
    "\n",
    "directors[\"Goldfinger\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "3e02b0b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Gaurav Sharma\\AppData\\Local\\Temp\\ipykernel_2916\\653851319.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  directors[\"Goldfinger\"] = \"Guy\"\n"
     ]
    }
   ],
   "source": [
    "directors[\"Goldfinger\"] = \"Guy\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "62e49b2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Film\n",
       "Dr. No                   Terence Young\n",
       "From Russia with Love    Terence Young\n",
       "Goldfinger                         Guy\n",
       "Name: Director, dtype: object"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here's the change\n",
    "\n",
    "directors.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "3b231537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year         Actor       Director  Box Office  Budget  \\\n",
       "Film                                                                           \n",
       "Dr. No                 1962  Sean Connery  Terence Young       448.8     7.0   \n",
       "From Russia with Love  1963  Sean Connery  Terence Young       543.8    12.6   \n",
       "Goldfinger             1964  Sean Connery            Guy       820.4    18.6   \n",
       "\n",
       "                       Bond Actor Salary  \n",
       "Film                                      \n",
       "Dr. No                               0.6  \n",
       "From Russia with Love                1.6  \n",
       "Goldfinger                           3.2  "
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# original also affected\n",
    "\n",
    "bond.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "c84e1367",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Film\n",
       "Dr. No                   Terence Young\n",
       "From Russia with Love    Terence Young\n",
       "Goldfinger                         Guy\n",
       "Name: Director, dtype: object"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this problem can be solved using copy() method\n",
    "\n",
    "directors = bond[\"Director\"].copy()\n",
    "directors.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "bc58a266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Terence Young'"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directors[\"Dr. No\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "43e85481",
   "metadata": {},
   "outputs": [],
   "source": [
    "directors[\"Dr. No\"] = \"Young\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "638a270d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Film\n",
       "Dr. No                           Young\n",
       "From Russia with Love    Terence Young\n",
       "Goldfinger                         Guy\n",
       "Name: Director, dtype: object"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directors.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "ab0d0b83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actor</th>\n",
       "      <th>Director</th>\n",
       "      <th>Box Office</th>\n",
       "      <th>Budget</th>\n",
       "      <th>Bond Actor Salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Film</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dr. No</th>\n",
       "      <td>1962</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>448.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Russia with Love</th>\n",
       "      <td>1963</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Terence Young</td>\n",
       "      <td>543.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldfinger</th>\n",
       "      <td>1964</td>\n",
       "      <td>Sean Connery</td>\n",
       "      <td>Guy</td>\n",
       "      <td>820.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Year         Actor       Director  Box Office  Budget  \\\n",
       "Film                                                                           \n",
       "Dr. No                 1962  Sean Connery  Terence Young       448.8     7.0   \n",
       "From Russia with Love  1963  Sean Connery  Terence Young       543.8    12.6   \n",
       "Goldfinger             1964  Sean Connery            Guy       820.4    18.6   \n",
       "\n",
       "                       Bond Actor Salary  \n",
       "Film                                      \n",
       "Dr. No                               0.6  \n",
       "From Russia with Love                1.6  \n",
       "Goldfinger                           3.2  "
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# original is safe\n",
    "\n",
    "bond.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0210f6b4",
   "metadata": {},
   "source": [
    "### <font color=brown> to be continued ...... </font>\n",
    "\n",
    "[Pandas - II] (http://localhost:8888/notebooks/Pandas%20-%20II.ipynb)"
   ]
  }
 ],
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