ENH: add value_counts to EA interface (#62254)

Author: jbrockmendel

ci/code_checks.sh

@@ -73,6 +73,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Period.freq GL08" \
         -i "pandas.Period.ordinal GL08" \
         -i "pandas.errors.IncompatibleFrequency SA01,SS06,EX01" \
+        -i "pandas.api.extensions.ExtensionArray.value_counts EX01,RT03,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.plot PR02" \
         -i "pandas.core.groupby.SeriesGroupBy.plot PR02" \
         -i "pandas.core.resample.Resampler.quantile PR01,PR07" \

pandas/core/arrays/base.py

@@ -99,7 +99,10 @@
         npt,
     )
 
-    from pandas import Index
+    from pandas import (
+        Index,
+        Series,
+    )
 
 _extension_array_shared_docs: dict[str, str] = {}
 
@@ -1673,6 +1676,25 @@ def repeat(self, repeats: int | Sequence[int], axis: AxisInt | None = None) -> S
         ind = np.arange(len(self)).repeat(repeats)
         return self.take(ind)
 
+    def value_counts(self, dropna: bool = True) -> Series:
+        """
+        Return a Series containing counts of unique values.
+
+        Parameters
+        ----------
+        dropna : bool, default True
+            Don't include counts of NA values.
+
+        Returns
+        -------
+        Series
+        """
+        from pandas.core.algorithms import value_counts_internal as value_counts
+
+        result = value_counts(self.to_numpy(copy=False), sort=False, dropna=dropna)
+        result.index = result.index.astype(self.dtype)
+        return result
+
     # ------------------------------------------------------------------------
     # Indexing methods
     # ------------------------------------------------------------------------

pandas/core/arrays/interval.py

@@ -75,7 +75,6 @@
     isin,
     take,
     unique,
-    value_counts_internal as value_counts,
 )
 from pandas.core.arrays import ArrowExtensionArray
 from pandas.core.arrays.base import (
@@ -105,7 +104,6 @@
 
     from pandas import (
         Index,
-        Series,
     )
 
 
@@ -1197,28 +1195,6 @@ def _validate_setitem_value(self, value):
 
         return value_left, value_right
 
-    def value_counts(self, dropna: bool = True) -> Series:
-        """
-        Returns a Series containing counts of each interval.
-
-        Parameters
-        ----------
-        dropna : bool, default True
-            Don't include counts of NaN.
-
-        Returns
-        -------
-        counts : Series
-
-        See Also
-        --------
-        Series.value_counts
-        """
-        # TODO: implement this is a non-naive way!
-        result = value_counts(np.asarray(self), dropna=dropna)
-        result.index = result.index.astype(self.dtype)
-        return result
-
     # ---------------------------------------------------------------------
     # Rendering Methods
 

pandas/core/arrays/string_.py

@@ -1037,10 +1037,7 @@ def sum(
         return self._wrap_reduction_result(axis, result)
 
     def value_counts(self, dropna: bool = True) -> Series:
-        from pandas.core.algorithms import value_counts_internal as value_counts
-
-        result = value_counts(self._ndarray, sort=False, dropna=dropna)
-        result.index = result.index.astype(self.dtype)
+        result = super().value_counts(dropna=dropna)
 
         if self.dtype.na_value is libmissing.NA:
             result = result.astype("Int64")

pandas/tests/extension/decimal/array.py

@@ -25,7 +25,6 @@
     is_scalar,
 )
 from pandas.core import arraylike
-from pandas.core.algorithms import value_counts_internal as value_counts
 from pandas.core.arraylike import OpsMixin
 from pandas.core.arrays import (
     ExtensionArray,
@@ -291,9 +290,6 @@ def convert_values(param):
 
         return np.asarray(res, dtype=bool)
 
-    def value_counts(self, dropna: bool = True):
-        return value_counts(self.to_numpy(), dropna=dropna)
-
     # We override fillna here to simulate a 3rd party EA that has done so. This
     #  lets us test a 3rd-party EA that has not yet updated to include a "copy"
     #  keyword in its fillna method.

pandas/tests/extension/decimal/test_decimal.py

@@ -171,26 +171,6 @@ def test_fillna_limit_series(self, data_missing):
         ):
             super().test_fillna_limit_series(data_missing)
 
-    @pytest.mark.parametrize("dropna", [True, False])
-    def test_value_counts(self, all_data, dropna):
-        all_data = all_data[:10]
-        if dropna:
-            other = np.array(all_data[~all_data.isna()])
-        else:
-            other = all_data
-
-        vcs = pd.Series(all_data).value_counts(dropna=dropna)
-        vcs_ex = pd.Series(other).value_counts(dropna=dropna)
-
-        with decimal.localcontext() as ctx:
-            # avoid raising when comparing Decimal("NAN") < Decimal(2)
-            ctx.traps[decimal.InvalidOperation] = False
-
-            result = vcs.sort_index()
-            expected = vcs_ex.sort_index()
-
-        tm.assert_series_equal(result, expected)
-
     def test_series_repr(self, data):
         # Overriding this base test to explicitly test that
         # the custom _formatter is used

pandas/tests/extension/json/test_json.py

@@ -189,14 +189,12 @@ def test_ffill_limit_area(
                 data_missing, limit_area, input_ilocs, expected_ilocs
             )
 
-    @unhashable
-    def test_value_counts(self, all_data, dropna):
+    def test_value_counts(self, all_data, dropna, request):
+        if len(all_data) == 100 or dropna:
+            mark = pytest.mark.xfail(reason="unhashable")
+            request.applymarker(mark)
         super().test_value_counts(all_data, dropna)
 
-    @unhashable
-    def test_value_counts_with_normalize(self, data):
-        super().test_value_counts_with_normalize(data)
-
     @unhashable
     def test_sort_values_frame(self):
         # TODO (EA.factorize): see if _values_for_factorize allows this.