06. Jupyter ipywidgets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 150px; float: left;\"> <img src=\"https://raw.githubusercontent.com/DataForScience/Networks/master/data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\" width=150px> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> <h1>Python Data Visualization for Everyone</h1>\n",
    "<h1>Jupyter ipywidgets</h1>\n",
    "        <p>Bruno Gonçalves<br/>\n",
    "        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n",
    "            @bgoncalves, @data4sci</p></div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "import ipywidgets as widgets\n",
    "\n",
    "import watermark\n",
    "\n",
    "%load_ext watermark\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by print out the versions of the libraries we're using for future reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.11.7\n",
      "IPython version      : 8.12.3\n",
      "\n",
      "Compiler    : Clang 14.0.6 \n",
      "OS          : Darwin\n",
      "Release     : 24.3.0\n",
      "Machine     : arm64\n",
      "Processor   : arm\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: fa8b92758a62ab3cc6cc750b820ec3ea509705e6\n",
      "\n",
      "pandas    : 1.5.0\n",
      "numpy     : 1.23.1\n",
      "watermark : 2.4.3\n",
      "matplotlib: 3.8.4\n",
      "ipywidgets: 8.1.5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic widgets with the Interact decorator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us load up some data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/gapminder.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And define some useful arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = np.array([\n",
    "    '#5A6FFA',\n",
    "    '#E60DA1',\n",
    "    \"#7E7E7E\",\n",
    "    \"#B7F025\",\n",
    "    \"#FF9A1E\"])\n",
    "\n",
    "continents = [\n",
    "    'Africa',\n",
    "    'Americas',\n",
    "    'Asia',\n",
    "    'Europe',\n",
    "    'Oceania']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And define a simple function to generate the simple plot we created in Lesson I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_continents():\n",
    "    fig, ax = plt.subplots(1, figsize=(14,10))\n",
    "\n",
    "    for i in range(5):\n",
    "        continent = data[data['Continent']==i]\n",
    "\n",
    "        ax.scatter(continent['GDP'], # x-axis\n",
    "                   continent['LifeExpectancy'], # y-axis\n",
    "                   s=continent['Population']/200000, # Bubble size\n",
    "                   c=colors[i], # Bubble color\n",
    "                   alpha=0.5) # Transparency\n",
    "\n",
    "    ax.set_xscale('log')\n",
    "\n",
    "    # Add the legend manually\n",
    "    for i in range(len(continents)):\n",
    "        ax.text(300, 83-i*2, continents[i], color=colors[i], fontsize=18)\n",
    "\n",
    "    ax.set_xlabel('GDP Per Capita')\n",
    "    ax.set_ylabel('Life Expectancy')\n",
    "    ax.set_xlim(200, 100000)\n",
    "    ax.set_ylim(40, 90)\n",
    "\n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_continents()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a dictionary to map continent names to integer IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cont_dict = dict(zip(continents, range(len(continents))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use __@widgets.interact__ to automatically generate widgets matching the function arguments. Here we pass the dictionary created above as the default argument. This will force interact to display the dictionary keys in the dropdown box, while passing the numerical value as input to the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e15779addba4955a363d87fd20d5077",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='cont', options={'Africa': 0, 'Americas': 1, 'Asia': 2, 'Europe': 3…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@widgets.interact\n",
    "def plot_continent(cont = cont_dict):\n",
    "    fig, ax = plt.subplots(1, figsize=(14,10))\n",
    "\n",
    "    continent = data[data['Continent']==cont]\n",
    "    \n",
    "    ax.scatter(continent['GDP'], # x-axis\n",
    "               continent['LifeExpectancy'], # y-axis\n",
    "               s=continent['Population']/200000, # Bubble size\n",
    "               c=colors[cont], # Bubble color\n",
    "               alpha=0.5) # Transparency\n",
    "\n",
    "    ax.set_xscale('log')\n",
    "\n",
    "    # Add the legend manually\n",
    "    ax.text(300, 83-cont*2, continents[cont], color=colors[cont], fontsize=18)\n",
    "\n",
    "    ax.set_xlabel('GDP Per Capita')\n",
    "    ax.set_ylabel('Life Expectancy')\n",
    "    ax.set_xlim(200, 100000)\n",
    "    ax.set_ylim(40, 90)\n",
    "\n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boolean arguments correspond to check boxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e1d08b6b6b9c4a31965103c7625f08d0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='cont', options={'Africa': 0, 'Americas': 1, 'Asia': 2, 'Europe': 3…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@widgets.interact\n",
    "def plot_continent(cont = cont_dict, log_scale=True):\n",
    "    fig, ax = plt.subplots(1, figsize=(14,10))\n",
    "\n",
    "    continent = data[data['Continent']==cont]\n",
    "    \n",
    "    ax.scatter(continent['GDP'], # x-axis\n",
    "               continent['LifeExpectancy'], # y-axis\n",
    "               s=continent['Population']/200000, # Bubble size\n",
    "               c=colors[cont], # Bubble color\n",
    "               alpha=0.5) # Transparency\n",
    "    \n",
    "    if log_scale:\n",
    "        ax.set_xscale('log')\n",
    "\n",
    "    # Add the legend manually\n",
    "    ax.text(300, 83-cont*2, continents[cont], color=colors[cont], fontsize=18)\n",
    "\n",
    "    ax.set_xlabel('GDP Per Capita')\n",
    "    ax.set_ylabel('Life Expectancy')\n",
    "    ax.set_xlim(200, 100000)\n",
    "    ax.set_ylim(40, 90)\n",
    "\n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add another parameter to our function to change the font size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "font_sizes = [10, 14, 18, 22, 26]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "interact helpfully adds a third parameter with the appropriate widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "23283a07b5ae4356b74bab270c6e685d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='cont', options={'Africa': 0, 'Americas': 1, 'Asia': 2, 'Europe': 3…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@widgets.interact\n",
    "def plot_continent(cont = cont_dict, log_scale=True, font_size=font_sizes):\n",
    "    fig, ax = plt.subplots(1, figsize=(14,10))\n",
    "\n",
    "    continent = data[data['Continent']==cont]\n",
    "    \n",
    "    ax.scatter(continent['GDP'], # x-axis\n",
    "               continent['LifeExpectancy'], # y-axis\n",
    "               s=continent['Population']/200000, # Bubble size\n",
    "               c=colors[cont], # Bubble color\n",
    "               alpha=0.5) # Transparency\n",
    "    \n",
    "    if log_scale:\n",
    "        ax.set_xscale('log')\n",
    "\n",
    "    # Add the legend manually\n",
    "    ax.text(300, 83-cont*2, \n",
    "            continents[cont], \n",
    "            color=colors[cont], \n",
    "            fontsize=font_size)\n",
    "\n",
    "    ax.set_xlabel('GDP Per Capita')\n",
    "    ax.set_ylabel('Life Expectancy')\n",
    "    ax.set_xlim(200, 100000)\n",
    "    ax.set_ylim(40, 90)\n",
    "\n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e884d38059e947e7b831f955d4a29fb4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='cont', options={'Africa': 0, 'Americas': 1, 'Asia': 2, 'Europe': 3…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widgets.interact(plot_continent, #The same function defined above\n",
    "                cont = cont_dict, \n",
    "                log_scale=True, \n",
    "                font_size=font_sizes);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a950faea86bc41ca9244d27b4d2991cb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='cont', options={'Africa': 0, 'Americas': 1, 'Asia': 2, 'Europe': 3…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widgets.interact_manual(plot_continent, \n",
    "                cont = cont_dict, \n",
    "                log_scale=True, \n",
    "                font_size=font_sizes);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we can also specify by hand which widget we want to use, including any options we like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "font_slider = widgets.IntSlider(\n",
    "    value=18,\n",
    "    min=10,\n",
    "    max=40,\n",
    "    step=2,\n",
    "    description='Font size:',\n",
    "    disabled=False,\n",
    "    \n",
    "    # Should the figure be updated as the value is being changed?\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    \n",
    "    # Show only decimal values\n",
    "    readout_format='d'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4631f2e11a8b4c399fea9ec6e00ffcd2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='cont', options={'Africa': 0, 'Americas': 1, 'Asia': 2, 'Europe': 3…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widgets.interact_manual(plot_continent, \n",
    "                 cont = cont_dict, \n",
    "                 log_scale=True, \n",
    "                 font_size=font_slider);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Connected widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Africa': 0, 'Americas': 1, 'Asia': 2, 'Europe': 3, 'Oceania': 4}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cont_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "continent_widget = widgets.Dropdown(\n",
    "    options=cont_dict,\n",
    "    value=3,\n",
    "    description='Continent:',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_font_slider = widgets.IntSlider(\n",
    "    value=18,\n",
    "    min=10,\n",
    "    max=40,\n",
    "    step=2,\n",
    "    description='Font size:',\n",
    "    disabled=False,\n",
    "    \n",
    "    # Should the figure be updated as the value is being changed?\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    \n",
    "    # Show only decimal values\n",
    "    readout_format='d'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_font_range(*args):\n",
    "    new_font_slider.max = 30 + 2.0 * continent_widget.value\n",
    "\n",
    "# Tell the continent widget to call update_font_range \n",
    "# whenever it's value changes\n",
    "continent_widget.observe(update_font_range, 'value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "253fbe89078d408c9a7f110aeb09468d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Continent:', index=3, options={'Africa': 0, 'Americas': 1, 'Asia':…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use interact as a function instead of a decorator\n",
    "widgets.interact(plot_continent, \n",
    "                 cont = continent_widget, \n",
    "                 log_scale=True, \n",
    "                 font_size=new_font_slider);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "     <img src=\"data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" border=\"0\" width=300px> \n",
    "</center>"
   ]
  }
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