CLN: move safe_sort from core.algorithms to core.sorting

pandas/core/algorithms.py

@@ -30,7 +30,6 @@
 from pandas.core.dtypes.missing import isnull
 
 from pandas.core import common as com
-from pandas.compat import string_types
 from pandas._libs import algos, lib, hashtable as htable
 from pandas._libs.tslib import iNaT
 
@@ -431,104 +430,6 @@ def isin(comps, values):
     return f(comps, values)
 
 
-def safe_sort(values, labels=None, na_sentinel=-1, assume_unique=False):
-    """
-    Sort ``values`` and reorder corresponding ``labels``.
-    ``values`` should be unique if ``labels`` is not None.
-    Safe for use with mixed types (int, str), orders ints before strs.
-
-    .. versionadded:: 0.19.0
-
-    Parameters
-    ----------
-    values : list-like
-        Sequence; must be unique if ``labels`` is not None.
-    labels : list_like
-        Indices to ``values``. All out of bound indices are treated as
-        "not found" and will be masked with ``na_sentinel``.
-    na_sentinel : int, default -1
-        Value in ``labels`` to mark "not found".
-        Ignored when ``labels`` is None.
-    assume_unique : bool, default False
-        When True, ``values`` are assumed to be unique, which can speed up
-        the calculation. Ignored when ``labels`` is None.
-
-    Returns
-    -------
-    ordered : ndarray
-        Sorted ``values``
-    new_labels : ndarray
-        Reordered ``labels``; returned when ``labels`` is not None.
-
-    Raises
-    ------
-    TypeError
-        * If ``values`` is not list-like or if ``labels`` is neither None
-        nor list-like
-        * If ``values`` cannot be sorted
-    ValueError
-        * If ``labels`` is not None and ``values`` contain duplicates.
-    """
-    if not is_list_like(values):
-        raise TypeError("Only list-like objects are allowed to be passed to"
-                        "safe_sort as values")
-    values = np.asarray(values)
-
-    def sort_mixed(values):
-        # order ints before strings, safe in py3
-        str_pos = np.array([isinstance(x, string_types) for x in values],
-                           dtype=bool)
-        nums = np.sort(values[~str_pos])
-        strs = np.sort(values[str_pos])
-        return _ensure_object(np.concatenate([nums, strs]))
-
-    sorter = None
-    if compat.PY3 and lib.infer_dtype(values) == 'mixed-integer':
-        # unorderable in py3 if mixed str/int
-        ordered = sort_mixed(values)
-    else:
-        try:
-            sorter = values.argsort()
-            ordered = values.take(sorter)
-        except TypeError:
-            # try this anyway
-            ordered = sort_mixed(values)
-
-    # labels:
-
-    if labels is None:
-        return ordered
-
-    if not is_list_like(labels):
-        raise TypeError("Only list-like objects or None are allowed to be"
-                        "passed to safe_sort as labels")
-    labels = _ensure_platform_int(np.asarray(labels))
-
-    from pandas import Index
-    if not assume_unique and not Index(values).is_unique:
-        raise ValueError("values should be unique if labels is not None")
-
-    if sorter is None:
-        # mixed types
-        (hash_klass, _), values = _get_data_algo(values, _hashtables)
-        t = hash_klass(len(values))
-        t.map_locations(values)
-        sorter = _ensure_platform_int(t.lookup(ordered))
-
-    reverse_indexer = np.empty(len(sorter), dtype=np.int_)
-    reverse_indexer.put(sorter, np.arange(len(sorter)))
-
-    mask = (labels < -len(values)) | (labels >= len(values)) | \
-        (labels == na_sentinel)
-
-    # (Out of bound indices will be masked with `na_sentinel` next, so we may
-    # deal with them here without performance loss using `mode='wrap'`.)
-    new_labels = reverse_indexer.take(labels, mode='wrap')
-    np.putmask(new_labels, mask, na_sentinel)
-
-    return ordered, _ensure_platform_int(new_labels)
-
-
 def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None):
     """
     Encode input values as an enumerated type or categorical variable
@@ -568,6 +469,7 @@ def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None):
     uniques = uniques.to_array()
 
     if sort and len(uniques) > 0:
+        from pandas.core.sorting import safe_sort
         uniques, labels = safe_sort(uniques, labels, na_sentinel=na_sentinel,
                                     assume_unique=True)
 

pandas/core/indexes/base.py

@@ -48,6 +48,7 @@
 import pandas.core.dtypes.concat as _concat
 import pandas.core.missing as missing
 import pandas.core.algorithms as algos
+import pandas.core.sorting as sorting
 from pandas.io.formats.printing import pprint_thing
 from pandas.core.ops import _comp_method_OBJECT_ARRAY
 from pandas.core.strings import StringAccessorMixin
@@ -2306,7 +2307,7 @@ def difference(self, other):
                                   assume_unique=True)
         the_diff = this.values.take(label_diff)
         try:
-            the_diff = algos.safe_sort(the_diff)
+            the_diff = sorting.safe_sort(the_diff)
         except TypeError:
             pass
 
@@ -2366,7 +2367,7 @@ def symmetric_difference(self, other, result_name=None):
 
         the_diff = _concat._concat_compat([left_diff, right_diff])
         try:
-            the_diff = algos.safe_sort(the_diff)
+            the_diff = sorting.safe_sort(the_diff)
         except TypeError:
             pass
 

pandas/core/reshape/merge.py

@@ -38,6 +38,7 @@
 
 from pandas.core.sorting import is_int64_overflow_possible
 import pandas.core.algorithms as algos
+import pandas.core.sorting as sorting
 import pandas.core.common as com
 from pandas._libs import hashtable as libhashtable, join as libjoin, lib
 from pandas.errors import MergeError
@@ -1491,7 +1492,7 @@ def _sort_labels(uniques, left, right):
     l = len(left)
     labels = np.concatenate([left, right])
 
-    _, new_labels = algos.safe_sort(uniques, labels, na_sentinel=-1)
+    _, new_labels = sorting.safe_sort(uniques, labels, na_sentinel=-1)
     new_labels = _ensure_int64(new_labels)
     new_left, new_right = new_labels[:l], new_labels[l:]
 

pandas/core/sorting.py

@@ -1,12 +1,14 @@
 """ miscellaneous sorting / groupby utilities """
 
 import numpy as np
-from pandas.compat import long
+from pandas.compat import long, string_types, PY3
 from pandas.core.categorical import Categorical
 from pandas.core.dtypes.common import (
     _ensure_platform_int,
     _ensure_int64,
+    is_list_like,
     is_categorical_dtype)
+from pandas.core.dtypes.cast import infer_dtype_from_array
 from pandas.core.dtypes.missing import isnull
 import pandas.core.algorithms as algorithms
 from pandas._libs import lib, algos, hashtable
@@ -376,3 +378,107 @@ def _reorder_by_uniques(uniques, labels):
     uniques = algorithms.take_nd(uniques, sorter, allow_fill=False)
 
     return uniques, labels
+
+
+def safe_sort(values, labels=None, na_sentinel=-1, assume_unique=False):
+    """
+    Sort ``values`` and reorder corresponding ``labels``.
+    ``values`` should be unique if ``labels`` is not None.
+    Safe for use with mixed types (int, str), orders ints before strs.
+
+    .. versionadded:: 0.19.0
+
+    Parameters
+    ----------
+    values : list-like
+        Sequence; must be unique if ``labels`` is not None.
+    labels : list_like
+        Indices to ``values``. All out of bound indices are treated as
+        "not found" and will be masked with ``na_sentinel``.
+    na_sentinel : int, default -1
+        Value in ``labels`` to mark "not found".
+        Ignored when ``labels`` is None.
+    assume_unique : bool, default False
+        When True, ``values`` are assumed to be unique, which can speed up
+        the calculation. Ignored when ``labels`` is None.
+
+    Returns
+    -------
+    ordered : ndarray
+        Sorted ``values``
+    new_labels : ndarray
+        Reordered ``labels``; returned when ``labels`` is not None.
+
+    Raises
+    ------
+    TypeError
+        * If ``values`` is not list-like or if ``labels`` is neither None
+        nor list-like
+        * If ``values`` cannot be sorted
+    ValueError
+        * If ``labels`` is not None and ``values`` contain duplicates.
+    """
+    if not is_list_like(values):
+        raise TypeError("Only list-like objects are allowed to be passed to"
+                        "safe_sort as values")
+
+    if not isinstance(values, np.ndarray):
+
+        # don't convert to string types
+        dtype, _ = infer_dtype_from_array(values)
+        values = np.asarray(values, dtype=dtype)
+
+    def sort_mixed(values):
+        # order ints before strings, safe in py3
+        str_pos = np.array([isinstance(x, string_types) for x in values],
+                           dtype=bool)
+        nums = np.sort(values[~str_pos])
+        strs = np.sort(values[str_pos])
+        return np.concatenate([nums, np.asarray(strs, dtype=object)])
+
+    sorter = None
+    if PY3 and lib.infer_dtype(values) == 'mixed-integer':
+        # unorderable in py3 if mixed str/int
+        ordered = sort_mixed(values)
+    else:
+        try:
+            sorter = values.argsort()
+            ordered = values.take(sorter)
+        except TypeError:
+            # try this anyway
+            ordered = sort_mixed(values)
+
+    # labels:
+
+    if labels is None:
+        return ordered
+
+    if not is_list_like(labels):
+        raise TypeError("Only list-like objects or None are allowed to be"
+                        "passed to safe_sort as labels")
+    labels = _ensure_platform_int(np.asarray(labels))
+
+    from pandas import Index
+    if not assume_unique and not Index(values).is_unique:
+        raise ValueError("values should be unique if labels is not None")
+
+    if sorter is None:
+        # mixed types
+        (hash_klass, _), values = algorithms._get_data_algo(
+            values, algorithms._hashtables)
+        t = hash_klass(len(values))
+        t.map_locations(values)
+        sorter = _ensure_platform_int(t.lookup(ordered))
+
+    reverse_indexer = np.empty(len(sorter), dtype=np.int_)
+    reverse_indexer.put(sorter, np.arange(len(sorter)))
+
+    mask = (labels < -len(values)) | (labels >= len(values)) | \
+        (labels == na_sentinel)
+
+    # (Out of bound indices will be masked with `na_sentinel` next, so we may
+    # deal with them here without performance loss using `mode='wrap'`.)
+    new_labels = reverse_indexer.take(labels, mode='wrap')
+    np.putmask(new_labels, mask, na_sentinel)
+
+    return ordered, _ensure_platform_int(new_labels)

pandas/tests/test_algos.py

@@ -2,7 +2,6 @@
 
 import numpy as np
 import pytest
-import warnings
 
 from numpy.random import RandomState
 from numpy import nan
@@ -60,93 +59,6 @@ def test_strings(self):
         tm.assert_series_equal(result, expected)
 
 
-class TestSafeSort(object):
-
-    def test_basic_sort(self):
-        values = [3, 1, 2, 0, 4]
-        result = algos.safe_sort(values)
-        expected = np.array([0, 1, 2, 3, 4])
-        tm.assert_numpy_array_equal(result, expected)
-
-        values = list("baaacb")
-        result = algos.safe_sort(values)
-        expected = np.array(list("aaabbc"))
-        tm.assert_numpy_array_equal(result, expected)
-
-        values = []
-        result = algos.safe_sort(values)
-        expected = np.array([])
-        tm.assert_numpy_array_equal(result, expected)
-
-    def test_labels(self):
-        values = [3, 1, 2, 0, 4]
-        expected = np.array([0, 1, 2, 3, 4])
-
-        labels = [0, 1, 1, 2, 3, 0, -1, 4]
-        result, result_labels = algos.safe_sort(values, labels)
-        expected_labels = np.array([3, 1, 1, 2, 0, 3, -1, 4], dtype=np.intp)
-        tm.assert_numpy_array_equal(result, expected)
-        tm.assert_numpy_array_equal(result_labels, expected_labels)
-
-        # na_sentinel
-        labels = [0, 1, 1, 2, 3, 0, 99, 4]
-        result, result_labels = algos.safe_sort(values, labels,
-                                                na_sentinel=99)
-        expected_labels = np.array([3, 1, 1, 2, 0, 3, 99, 4], dtype=np.intp)
-        tm.assert_numpy_array_equal(result, expected)
-        tm.assert_numpy_array_equal(result_labels, expected_labels)
-
-        # out of bound indices
-        labels = [0, 101, 102, 2, 3, 0, 99, 4]
-        result, result_labels = algos.safe_sort(values, labels)
-        expected_labels = np.array([3, -1, -1, 2, 0, 3, -1, 4], dtype=np.intp)
-        tm.assert_numpy_array_equal(result, expected)
-        tm.assert_numpy_array_equal(result_labels, expected_labels)
-
-        labels = []
-        result, result_labels = algos.safe_sort(values, labels)
-        expected_labels = np.array([], dtype=np.intp)
-        tm.assert_numpy_array_equal(result, expected)
-        tm.assert_numpy_array_equal(result_labels, expected_labels)
-
-    def test_mixed_integer(self):
-        values = np.array(['b', 1, 0, 'a', 0, 'b'], dtype=object)
-        result = algos.safe_sort(values)
-        expected = np.array([0, 0, 1, 'a', 'b', 'b'], dtype=object)
-        tm.assert_numpy_array_equal(result, expected)
-
-        values = np.array(['b', 1, 0, 'a'], dtype=object)
-        labels = [0, 1, 2, 3, 0, -1, 1]
-        result, result_labels = algos.safe_sort(values, labels)
-        expected = np.array([0, 1, 'a', 'b'], dtype=object)
-        expected_labels = np.array([3, 1, 0, 2, 3, -1, 1], dtype=np.intp)
-        tm.assert_numpy_array_equal(result, expected)
-        tm.assert_numpy_array_equal(result_labels, expected_labels)
-
-    def test_unsortable(self):
-        # GH 13714
-        arr = np.array([1, 2, datetime.now(), 0, 3], dtype=object)
-        if compat.PY2 and not pd._np_version_under1p10:
-            # RuntimeWarning: tp_compare didn't return -1 or -2 for exception
-            with warnings.catch_warnings():
-                pytest.raises(TypeError, algos.safe_sort, arr)
-        else:
-            pytest.raises(TypeError, algos.safe_sort, arr)
-
-    def test_exceptions(self):
-        with tm.assert_raises_regex(TypeError,
-                                    "Only list-like objects are allowed"):
-            algos.safe_sort(values=1)
-
-        with tm.assert_raises_regex(TypeError,
-                                    "Only list-like objects or None"):
-            algos.safe_sort(values=[0, 1, 2], labels=1)
-
-        with tm.assert_raises_regex(ValueError,
-                                    "values should be unique"):
-            algos.safe_sort(values=[0, 1, 2, 1], labels=[0, 1])
-
-
 class TestFactorize(object):
 
     def test_basic(self):