CLN: handle EAs and fast path (no bounds checking) in safe_sort (#25696)

doc/source/whatsnew/v0.25.0.rst

@@ -428,6 +428,7 @@ Other
 ^^^^^
 
 - Removed unused C functions from vendored UltraJSON implementation (:issue:`26198`)
+- Bug in :func:`factorize` when passing an ``ExtensionArray`` with a custom ``na_sentinel`` (:issue:`25696`).
 
 
 .. _whatsnew_0.250.contributors:

pandas/core/algorithms.py

@@ -617,22 +617,8 @@ def factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None):
 
     if sort and len(uniques) > 0:
         from pandas.core.sorting import safe_sort
-        if na_sentinel == -1:
-            # GH-25409 take_1d only works for na_sentinels of -1
-            try:
-                order = uniques.argsort()
-                order2 = order.argsort()
-                labels = take_1d(order2, labels, fill_value=na_sentinel)
-                uniques = uniques.take(order)
-            except TypeError:
-                # Mixed types, where uniques.argsort fails.
-                uniques, labels = safe_sort(uniques, labels,
-                                            na_sentinel=na_sentinel,
-                                            assume_unique=True)
-        else:
-            uniques, labels = safe_sort(uniques, labels,
-                                        na_sentinel=na_sentinel,
-                                        assume_unique=True)
+        uniques, labels = safe_sort(uniques, labels, na_sentinel=na_sentinel,
+                                    assume_unique=True, verify=False)
 
     uniques = _reconstruct_data(uniques, dtype, original)
 

pandas/core/sorting.py

@@ -8,7 +8,8 @@
 
 from pandas.core.dtypes.cast import infer_dtype_from_array
 from pandas.core.dtypes.common import (
-    ensure_int64, ensure_platform_int, is_categorical_dtype, is_list_like)
+    ensure_int64, ensure_platform_int, is_categorical_dtype,
+    is_extension_array_dtype, is_list_like)
 from pandas.core.dtypes.missing import isna
 
 import pandas.core.algorithms as algorithms
@@ -403,7 +404,8 @@ def _reorder_by_uniques(uniques, labels):
     return uniques, labels
 
 
-def safe_sort(values, labels=None, na_sentinel=-1, assume_unique=False):
+def safe_sort(values, labels=None, na_sentinel=-1, assume_unique=False,
+              verify=True):
     """
     Sort ``values`` and reorder corresponding ``labels``.
     ``values`` should be unique if ``labels`` is not None.
@@ -424,6 +426,12 @@ def safe_sort(values, labels=None, na_sentinel=-1, assume_unique=False):
     assume_unique : bool, default False
         When True, ``values`` are assumed to be unique, which can speed up
         the calculation. Ignored when ``labels`` is None.
+    verify : bool, default True
+        Check if labels are out of bound for the values and put out of bound
+        labels equal to na_sentinel. If ``verify=False``, it is assumed there
+        are no out of bound labels. Ignored when ``labels`` is None.
+
+        .. versionadded:: 0.25.0
 
     Returns
     -------
@@ -445,8 +453,8 @@ def safe_sort(values, labels=None, na_sentinel=-1, assume_unique=False):
         raise TypeError("Only list-like objects are allowed to be passed to"
                         "safe_sort as values")
 
-    if not isinstance(values, np.ndarray):
-
+    if (not isinstance(values, np.ndarray)
+            and not is_extension_array_dtype(values)):
         # don't convert to string types
         dtype, _ = infer_dtype_from_array(values)
         values = np.asarray(values, dtype=dtype)
@@ -460,7 +468,8 @@ def sort_mixed(values):
         return np.concatenate([nums, np.asarray(strs, dtype=object)])
 
     sorter = None
-    if lib.infer_dtype(values, skipna=False) == 'mixed-integer':
+    if (not is_extension_array_dtype(values)
+            and lib.infer_dtype(values, skipna=False) == 'mixed-integer'):
         # unorderable in py3 if mixed str/int
         ordered = sort_mixed(values)
     else:
@@ -493,15 +502,26 @@ def sort_mixed(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)
+    if na_sentinel == -1:
+        # take_1d is faster, but only works for na_sentinels of -1
+        order2 = sorter.argsort()
+        new_labels = algorithms.take_1d(order2, labels, fill_value=-1)
+        if verify:
+            mask = (labels < -len(values)) | (labels >= len(values))
+        else:
+            mask = None
+    else:
+        reverse_indexer = np.empty(len(sorter), dtype=np.int_)
+        reverse_indexer.put(sorter, np.arange(len(sorter)))
+        # 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')
+
+        mask = labels == na_sentinel
+        if verify:
+            mask = mask | (labels < -len(values)) | (labels >= len(values))
+
+    if mask is not None:
+        np.putmask(new_labels, mask, na_sentinel)
 
     return ordered, ensure_platform_int(new_labels)

pandas/tests/test_algos.py

@@ -21,6 +21,7 @@
 import pandas.core.algorithms as algos
 from pandas.core.arrays import DatetimeArray
 import pandas.core.common as com
+from pandas.core.sorting import safe_sort
 import pandas.util.testing as tm
 from pandas.util.testing import assert_almost_equal
 
@@ -324,18 +325,26 @@ def test_parametrized_factorize_na_value(self, data, na_value):
 
     @pytest.mark.parametrize('sort', [True, False])
     @pytest.mark.parametrize('na_sentinel', [-1, -10, 100])
-    def test_factorize_na_sentinel(self, sort, na_sentinel):
-        data = np.array(['b', 'a', None, 'b'], dtype=object)
+    @pytest.mark.parametrize('data, uniques', [
+        (np.array(['b', 'a', None, 'b'], dtype=object),
+         np.array(['b', 'a'], dtype=object)),
+        (pd.array([2, 1, np.nan, 2], dtype='Int64'),
+         pd.array([2, 1], dtype='Int64'))],
+        ids=['numpy_array', 'extension_array'])
+    def test_factorize_na_sentinel(self, sort, na_sentinel, data, uniques):
         labels, uniques = algos.factorize(data, sort=sort,
                                           na_sentinel=na_sentinel)
         if sort:
             expected_labels = np.array([1, 0, na_sentinel, 1], dtype=np.intp)
-            expected_uniques = np.array(['a', 'b'], dtype=object)
+            expected_uniques = safe_sort(uniques)
         else:
             expected_labels = np.array([0, 1, na_sentinel, 0], dtype=np.intp)
-            expected_uniques = np.array(['b', 'a'], dtype=object)
+            expected_uniques = uniques
         tm.assert_numpy_array_equal(labels, expected_labels)
-        tm.assert_numpy_array_equal(uniques, expected_uniques)
+        if isinstance(data, np.ndarray):
+            tm.assert_numpy_array_equal(uniques, expected_uniques)
+        else:
+            tm.assert_extension_array_equal(uniques, expected_uniques)
 
 
 class TestUnique:

pandas/tests/test_sorting.py

@@ -6,7 +6,8 @@
 from numpy import nan
 import pytest
 
-from pandas import DataFrame, MultiIndex, Series, concat, merge, to_datetime
+from pandas import (
+    DataFrame, MultiIndex, Series, array, concat, merge, to_datetime)
 from pandas.core import common as com
 from pandas.core.sorting import (
     decons_group_index, get_group_index, is_int64_overflow_possible,
@@ -358,34 +359,43 @@ def test_basic_sort(self):
         expected = np.array([])
         tm.assert_numpy_array_equal(result, expected)
 
-    def test_labels(self):
+    @pytest.mark.parametrize('verify', [True, False])
+    def test_labels(self, verify):
         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 = safe_sort(values, labels)
+        result, result_labels = safe_sort(values, labels, verify=verify)
         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 = safe_sort(values, labels,
-                                          na_sentinel=99)
+        result, result_labels = safe_sort(values, labels, na_sentinel=99,
+                                          verify=verify)
         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 = safe_sort(values, labels)
-        expected_labels = np.array([3, -1, -1, 2, 0, 3, -1, 4], dtype=np.intp)
+        labels = []
+        result, result_labels = safe_sort(values, labels, verify=verify)
+        expected_labels = np.array([], dtype=np.intp)
         tm.assert_numpy_array_equal(result, expected)
         tm.assert_numpy_array_equal(result_labels, expected_labels)
 
-        labels = []
-        result, result_labels = safe_sort(values, labels)
-        expected_labels = np.array([], dtype=np.intp)
+    @pytest.mark.parametrize('na_sentinel', [-1, 99])
+    def test_labels_out_of_bound(self, na_sentinel):
+        values = [3, 1, 2, 0, 4]
+        expected = np.array([0, 1, 2, 3, 4])
+
+        # out of bound indices
+        labels = [0, 101, 102, 2, 3, 0, 99, 4]
+        result, result_labels = safe_sort(
+            values, labels, na_sentinel=na_sentinel)
+        expected_labels = np.array(
+            [3, na_sentinel, na_sentinel, 2, 0, 3, na_sentinel, 4],
+            dtype=np.intp)
         tm.assert_numpy_array_equal(result, expected)
         tm.assert_numpy_array_equal(result_labels, expected_labels)
 
@@ -430,3 +440,22 @@ def test_exceptions(self):
         with pytest.raises(ValueError,
                            match="values should be unique"):
             safe_sort(values=[0, 1, 2, 1], labels=[0, 1])
+
+    def test_extension_array(self):
+        # a = array([1, 3, np.nan, 2], dtype='Int64')
+        a = array([1, 3, 2], dtype='Int64')
+        result = safe_sort(a)
+        # expected = array([1, 2, 3, np.nan], dtype='Int64')
+        expected = array([1, 2, 3], dtype='Int64')
+        tm.assert_extension_array_equal(result, expected)
+
+    @pytest.mark.parametrize('verify', [True, False])
+    @pytest.mark.parametrize('na_sentinel', [-1, 99])
+    def test_extension_array_labels(self, verify, na_sentinel):
+        a = array([1, 3, 2], dtype='Int64')
+        result, labels = safe_sort(a, [0, 1, na_sentinel, 2],
+                                   na_sentinel=na_sentinel, verify=verify)
+        expected_values = array([1, 2, 3], dtype='Int64')
+        expected_labels = np.array([0, 2, na_sentinel, 1], dtype=np.intp)
+        tm.assert_extension_array_equal(result, expected_values)
+        tm.assert_numpy_array_equal(labels, expected_labels)