Backport PR #51542 on branch 2.0.x (API: ArrowExtensionArray.value_co…

…unts returns pyarrow.int64 type) (#51620)

Backport PR #51542: API: ArrowExtensionArray.value_counts returns pyarrow.int64 type

Co-authored-by: Matthew Roeschke <10647082+mroeschke@users.noreply.github.com>

doc/source/whatsnew/v2.0.0.rst

@@ -818,6 +818,7 @@ Other API changes
 - :class:`DataFrame` and :class:`DataFrameGroupBy` aggregations (e.g. "sum") with object-dtype columns no longer infer non-object dtypes for their results, explicitly call ``result.infer_objects(copy=False)`` on the result to obtain the old behavior (:issue:`51205`, :issue:`49603`)
 - Division by zero with :class:`ArrowDtype` dtypes returns ``-inf``, ``nan``, or ``inf`` depending on the numerator, instead of raising (:issue:`51541`)
 - Added :func:`pandas.api.types.is_any_real_numeric_dtype` to check for real numeric dtypes (:issue:`51152`)
+- :meth:`~arrays.ArrowExtensionArray.value_counts` now returns data with :class:`ArrowDtype` with ``pyarrow.int64`` type instead of ``"Int64"`` type (:issue:`51462`)
 
 .. note::
 

pandas/core/algorithms.py

@@ -873,6 +873,9 @@ def value_counts(
             result.name = name
             result.index.name = index_name
             counts = result._values
+            if not isinstance(counts, np.ndarray):
+                # e.g. ArrowExtensionArray
+                counts = np.asarray(counts)
 
         elif isinstance(values, ABCMultiIndex):
             # GH49558

pandas/core/arrays/arrow/array.py

@@ -983,12 +983,11 @@ def value_counts(self, dropna: bool = True) -> Series:
         if pa.types.is_duration(pa_type):
             values = values.cast(pa_type)
 
-        # No missing values so we can adhere to the interface and return a numpy array.
-        counts = np.array(counts)
+        counts = ArrowExtensionArray(counts)
 
         index = Index(type(self)(values))
 
-        return Series(counts, index=index, name="count").astype("Int64")
+        return Series(counts, index=index, name="count")
 
     @classmethod
     def _concat_same_type(

pandas/tests/arrays/string_/test_string.py

@@ -453,20 +453,28 @@ def test_arrow_load_from_zero_chunks(dtype, string_storage2):
 
 
 def test_value_counts_na(dtype):
+    if getattr(dtype, "storage", "") == "pyarrow":
+        exp_dtype = "int64[pyarrow]"
+    else:
+        exp_dtype = "Int64"
     arr = pd.array(["a", "b", "a", pd.NA], dtype=dtype)
     result = arr.value_counts(dropna=False)
-    expected = pd.Series([2, 1, 1], index=arr[[0, 1, 3]], dtype="Int64", name="count")
+    expected = pd.Series([2, 1, 1], index=arr[[0, 1, 3]], dtype=exp_dtype, name="count")
     tm.assert_series_equal(result, expected)
 
     result = arr.value_counts(dropna=True)
-    expected = pd.Series([2, 1], index=arr[:2], dtype="Int64", name="count")
+    expected = pd.Series([2, 1], index=arr[:2], dtype=exp_dtype, name="count")
     tm.assert_series_equal(result, expected)
 
 
 def test_value_counts_with_normalize(dtype):
+    if getattr(dtype, "storage", "") == "pyarrow":
+        exp_dtype = "double[pyarrow]"
+    else:
+        exp_dtype = "Float64"
     ser = pd.Series(["a", "b", "a", pd.NA], dtype=dtype)
     result = ser.value_counts(normalize=True)
-    expected = pd.Series([2, 1], index=ser[:2], dtype="Float64", name="proportion") / 3
+    expected = pd.Series([2, 1], index=ser[:2], dtype=exp_dtype, name="proportion") / 3
     tm.assert_series_equal(result, expected)
 
 

pandas/tests/base/test_value_counts.py

@@ -39,8 +39,11 @@ def test_value_counts(index_or_series_obj):
         expected.index.name = obj.name
 
     if not isinstance(result.dtype, np.dtype):
-        # i.e IntegerDtype
-        expected = expected.astype("Int64")
+        if getattr(obj.dtype, "storage", "") == "pyarrow":
+            expected = expected.astype("int64[pyarrow]")
+        else:
+            # i.e IntegerDtype
+            expected = expected.astype("Int64")
 
     # TODO(GH#32514): Order of entries with the same count is inconsistent
     #  on CI (gh-32449)
@@ -90,8 +93,11 @@ def test_value_counts_null(null_obj, index_or_series_obj):
         result = result.sort_index()
 
     if not isinstance(result.dtype, np.dtype):
-        # i.e IntegerDtype
-        expected = expected.astype("Int64")
+        if getattr(obj.dtype, "storage", "") == "pyarrow":
+            expected = expected.astype("int64[pyarrow]")
+        else:
+            # i.e IntegerDtype
+            expected = expected.astype("Int64")
     tm.assert_series_equal(result, expected)
 
     expected[null_obj] = 3

pandas/tests/extension/test_arrow.py

@@ -793,9 +793,25 @@ def test_diff(self, data, periods, request):
             )
         super().test_diff(data, periods)
 
-    @pytest.mark.parametrize("dropna", [True, False])
-    def test_value_counts(self, all_data, dropna, request):
-        super().test_value_counts(all_data, dropna)
+    def test_value_counts_returns_pyarrow_int64(self, data):
+        # GH 51462
+        data = data[:10]
+        result = data.value_counts()
+        assert result.dtype == ArrowDtype(pa.int64())
+
+    def test_value_counts_with_normalize(self, data, request):
+        data = data[:10].unique()
+        values = np.array(data[~data.isna()])
+        ser = pd.Series(data, dtype=data.dtype)
+
+        result = ser.value_counts(normalize=True).sort_index()
+
+        expected = pd.Series(
+            [1 / len(values)] * len(values), index=result.index, name="proportion"
+        )
+        expected = expected.astype("double[pyarrow]")
+
+        self.assert_series_equal(result, expected)
 
     def test_argmin_argmax(
         self, data_for_sorting, data_missing_for_sorting, na_value, request

pandas/tests/extension/test_string.py

@@ -18,7 +18,6 @@
 import numpy as np
 import pytest
 
-from pandas.compat import pa_version_under7p0
 from pandas.errors import PerformanceWarning
 
 import pandas as pd
@@ -196,70 +195,20 @@ def test_reduce_series_numeric(self, data, all_numeric_reductions, skipna):
 
 
 class TestMethods(base.BaseMethodsTests):
-    def test_argsort(self, data_for_sorting):
-        with tm.maybe_produces_warning(
-            PerformanceWarning,
-            pa_version_under7p0
-            and getattr(data_for_sorting.dtype, "storage", "") == "pyarrow",
-            check_stacklevel=False,
-        ):
-            super().test_argsort(data_for_sorting)
+    def test_value_counts_with_normalize(self, data):
+        data = data[:10].unique()
+        values = np.array(data[~data.isna()])
+        ser = pd.Series(data, dtype=data.dtype)
 
-    def test_argsort_missing(self, data_missing_for_sorting):
-        with tm.maybe_produces_warning(
-            PerformanceWarning,
-            pa_version_under7p0
-            and getattr(data_missing_for_sorting.dtype, "storage", "") == "pyarrow",
-            check_stacklevel=False,
-        ):
-            super().test_argsort_missing(data_missing_for_sorting)
-
-    def test_argmin_argmax(
-        self, data_for_sorting, data_missing_for_sorting, na_value, request
-    ):
-        super().test_argmin_argmax(data_for_sorting, data_missing_for_sorting, na_value)
-
-    @pytest.mark.parametrize(
-        "op_name, skipna, expected",
-        [
-            ("idxmax", True, 0),
-            ("idxmin", True, 2),
-            ("argmax", True, 0),
-            ("argmin", True, 2),
-            ("idxmax", False, np.nan),
-            ("idxmin", False, np.nan),
-            ("argmax", False, -1),
-            ("argmin", False, -1),
-        ],
-    )
-    def test_argreduce_series(
-        self, data_missing_for_sorting, op_name, skipna, expected, request
-    ):
-        super().test_argreduce_series(
-            data_missing_for_sorting, op_name, skipna, expected
-        )
+        result = ser.value_counts(normalize=True).sort_index()
 
-    @pytest.mark.parametrize("dropna", [True, False])
-    def test_value_counts(self, all_data, dropna, request):
-        all_data = all_data[:10]
-        if dropna:
-            other = all_data[~all_data.isna()]
+        expected = pd.Series(
+            [1 / len(values)] * len(values), index=result.index, name="proportion"
+        )
+        if getattr(data.dtype, "storage", "") == "pyarrow":
+            expected = expected.astype("double[pyarrow]")
         else:
-            other = all_data
-        with tm.maybe_produces_warning(
-            PerformanceWarning,
-            pa_version_under7p0
-            and getattr(all_data.dtype, "storage", "") == "pyarrow"
-            and not (dropna and "data_missing" in request.node.nodeid),
-        ):
-            result = pd.Series(all_data).value_counts(dropna=dropna).sort_index()
-        with tm.maybe_produces_warning(
-            PerformanceWarning,
-            pa_version_under7p0
-            and getattr(other.dtype, "storage", "") == "pyarrow"
-            and not (dropna and "data_missing" in request.node.nodeid),
-        ):
-            expected = pd.Series(other).value_counts(dropna=dropna).sort_index()
+            expected = expected.astype("Float64")
 
         self.assert_series_equal(result, expected)