ENH/TST: Add BaseMethodsTests tests for ArrowExtensionArray (#47552)

* ENH/TST: Add BaseMethodsTests tests for ArrowExtensionArray * Passing test now * add xfails for arraymanager * Fix typo * Trigger CI * Add xfails for min version and datamanger * Adjust more tests

Author: mroeschke

pandas/core/base.py

@@ -983,10 +983,12 @@ def unique(self):
 
  if not isinstance(values, np.ndarray):
  result: ArrayLike = values.unique()
- if self.dtype.kind in ["m", "M"] and isinstance(self, ABCSeries):
- # GH#31182 Series._values returns EA, unpack for backward-compat
- if getattr(self.dtype, "tz", None) is None:
- result = np.asarray(result)
+ if (
+ isinstance(self.dtype, np.dtype) and self.dtype.kind in ["m", "M"]
+ ) and isinstance(self, ABCSeries):
+ # GH#31182 Series._values returns EA
+ # unpack numpy datetime for backward-compat
+ result = np.asarray(result)
  else:
  result = unique1d(values)
 

pandas/tests/extension/base/methods.py

@@ -5,6 +5,7 @@
 import pytest
 
 from pandas.core.dtypes.common import is_bool_dtype
+from pandas.core.dtypes.missing import na_value_for_dtype
 
 import pandas as pd
 import pandas._testing as tm
@@ -49,8 +50,7 @@ def test_value_counts_with_normalize(self, data):
  else:
  expected = pd.Series(0.0, index=result.index)
  expected[result > 0] = 1 / len(values)
-
- if isinstance(data.dtype, pd.core.dtypes.dtypes.BaseMaskedDtype):
+ if na_value_for_dtype(data.dtype) is pd.NA:
  # TODO(GH#44692): avoid special-casing
  expected = expected.astype("Float64")
 

pandas/tests/extension/test_arrow.py

@@ -153,6 +153,32 @@ def data_for_grouping(dtype):
  return pd.array([B, B, None, None, A, A, B, C], dtype=dtype)
 
 
+@pytest.fixture
+def data_for_sorting(data_for_grouping):
+ """
+ Length-3 array with a known sort order.
+
+ This should be three items [B, C, A] with
+ A < B < C
+ """
+ return type(data_for_grouping)._from_sequence(
+ [data_for_grouping[0], data_for_grouping[7], data_for_grouping[4]]
+ )
+
+
+@pytest.fixture
+def data_missing_for_sorting(data_for_grouping):
+ """
+ Length-3 array with a known sort order.
+
+ This should be three items [B, NA, A] with
+ A < B and NA missing.
+ """
+ return type(data_for_grouping)._from_sequence(
+ [data_for_grouping[0], data_for_grouping[2], data_for_grouping[4]]
+ )
+
+
 @pytest.fixture
 def na_value():
  """The scalar missing value for this type. Default 'None'"""
@@ -654,7 +680,7 @@ def test_setitem_loc_scalar_single(self, data, using_array_manager, request):
  if pa_version_under2p0 and tz not in (None, "UTC"):
  request.node.add_marker(
  pytest.mark.xfail(
- reason=(f"Not supported by pyarrow < 2.0 with timestamp type {tz}")
+ reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}"
  )
  )
  elif using_array_manager and pa.types.is_duration(data.dtype.pyarrow_dtype):
@@ -988,6 +1014,287 @@ def test_EA_types(self, engine, data, request):
  super().test_EA_types(engine, data)
 
 
+class TestBaseMethods(base.BaseMethodsTests):
+ @pytest.mark.parametrize("dropna", [True, False])
+ def test_value_counts(self, all_data, dropna, request):
+ pa_dtype = all_data.dtype.pyarrow_dtype
+ if pa.types.is_date(pa_dtype) or (
+ pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is None
+ ):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ raises=AttributeError,
+ reason="GH 34986",
+ )
+ )
+ elif pa.types.is_duration(pa_dtype):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ raises=pa.ArrowNotImplementedError,
+ reason=f"value_count has no kernel for {pa_dtype}",
+ )
+ )
+ super().test_value_counts(all_data, dropna)
+
+ def test_value_counts_with_normalize(self, data, request):
+ pa_dtype = data.dtype.pyarrow_dtype
+ if pa.types.is_date(pa_dtype) or (
+ pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is None
+ ):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ raises=AttributeError,
+ reason="GH 34986",
+ )
+ )
+ elif pa.types.is_duration(pa_dtype):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ raises=pa.ArrowNotImplementedError,
+ reason=f"value_count has no pyarrow kernel for {pa_dtype}",
+ )
+ )
+ super().test_value_counts_with_normalize(data)
+
+ def test_argmin_argmax(
+ self, data_for_sorting, data_missing_for_sorting, na_value, request
+ ):
+ pa_dtype = data_for_sorting.dtype.pyarrow_dtype
+ if pa.types.is_boolean(pa_dtype):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason=f"{pa_dtype} only has 2 unique possible values",
+ )
+ )
+ super().test_argmin_argmax(data_for_sorting, data_missing_for_sorting, na_value)
+
+ @pytest.mark.parametrize("ascending", [True, False])
+ def test_sort_values(self, data_for_sorting, ascending, sort_by_key, request):
+ pa_dtype = data_for_sorting.dtype.pyarrow_dtype
+ if pa.types.is_duration(pa_dtype) and not ascending and not pa_version_under2p0:
+ request.node.add_marker(
+ pytest.mark.xfail(
+ raises=pa.ArrowNotImplementedError,
+ reason=(
+ f"unique has no pyarrow kernel "
+ f"for {pa_dtype} when ascending={ascending}"
+ ),
+ )
+ )
+ super().test_sort_values(data_for_sorting, ascending, sort_by_key)
+
+ @pytest.mark.parametrize("ascending", [True, False])
+ def test_sort_values_frame(self, data_for_sorting, ascending, request):
+ pa_dtype = data_for_sorting.dtype.pyarrow_dtype
+ if pa.types.is_duration(pa_dtype):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ raises=pa.ArrowNotImplementedError,
+ reason=(
+ f"dictionary_encode has no pyarrow kernel "
+ f"for {pa_dtype} when ascending={ascending}"
+ ),
+ )
+ )
+ super().test_sort_values_frame(data_for_sorting, ascending)
+
+ @pytest.mark.parametrize("box", [pd.Series, lambda x: x])
+ @pytest.mark.parametrize("method", [lambda x: x.unique(), pd.unique])
+ def test_unique(self, data, box, method, request):
+ pa_dtype = data.dtype.pyarrow_dtype
+ if pa.types.is_duration(pa_dtype) and not pa_version_under2p0:
+ request.node.add_marker(
+ pytest.mark.xfail(
+ raises=pa.ArrowNotImplementedError,
+ reason=f"unique has no pyarrow kernel for {pa_dtype}.",
+ )
+ )
+ super().test_unique(data, box, method)
+
+ @pytest.mark.parametrize("na_sentinel", [-1, -2])
+ def test_factorize(self, data_for_grouping, na_sentinel, request):
+ pa_dtype = data_for_grouping.dtype.pyarrow_dtype
+ if pa.types.is_duration(pa_dtype):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ raises=pa.ArrowNotImplementedError,
+ reason=f"dictionary_encode has no pyarrow kernel for {pa_dtype}",
+ )
+ )
+ elif pa.types.is_boolean(pa_dtype):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason=f"{pa_dtype} only has 2 unique possible values",
+ )
+ )
+ super().test_factorize(data_for_grouping, na_sentinel)
+
+ @pytest.mark.parametrize("na_sentinel", [-1, -2])
+ def test_factorize_equivalence(self, data_for_grouping, na_sentinel, request):
+ pa_dtype = data_for_grouping.dtype.pyarrow_dtype
+ if pa.types.is_duration(pa_dtype):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ raises=pa.ArrowNotImplementedError,
+ reason=f"dictionary_encode has no pyarrow kernel for {pa_dtype}",
+ )
+ )
+ super().test_factorize_equivalence(data_for_grouping, na_sentinel)
+
+ def test_factorize_empty(self, data, request):
+ pa_dtype = data.dtype.pyarrow_dtype
+ if pa.types.is_duration(pa_dtype):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ raises=pa.ArrowNotImplementedError,
+ reason=f"dictionary_encode has no pyarrow kernel for {pa_dtype}",
+ )
+ )
+ super().test_factorize_empty(data)
+
+ def test_fillna_copy_frame(self, data_missing, request, using_array_manager):
+ pa_dtype = data_missing.dtype.pyarrow_dtype
+ if using_array_manager and pa.types.is_duration(pa_dtype):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason=f"Checking ndim when using arraymanager with {pa_dtype}"
+ )
+ )
+ super().test_fillna_copy_frame(data_missing)
+
+ def test_fillna_copy_series(self, data_missing, request, using_array_manager):
+ pa_dtype = data_missing.dtype.pyarrow_dtype
+ if using_array_manager and pa.types.is_duration(pa_dtype):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason=f"Checking ndim when using arraymanager with {pa_dtype}"
+ )
+ )
+ super().test_fillna_copy_series(data_missing)
+
+ def test_shift_fill_value(self, data, request):
+ pa_dtype = data.dtype.pyarrow_dtype
+ tz = getattr(pa_dtype, "tz", None)
+ if pa_version_under2p0 and tz not in (None, "UTC"):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}"
+ )
+ )
+ super().test_shift_fill_value(data)
+
+ @pytest.mark.parametrize("repeats", [0, 1, 2, [1, 2, 3]])
+ def test_repeat(self, data, repeats, as_series, use_numpy, request):
+ pa_dtype = data.dtype.pyarrow_dtype
+ tz = getattr(pa_dtype, "tz", None)
+ if pa_version_under2p0 and tz not in (None, "UTC") and repeats != 0:
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason=(
+ f"Not supported by pyarrow < 2.0 with "
+ f"timestamp type {tz} when repeats={repeats}"
+ )
+ )
+ )
+ super().test_repeat(data, repeats, as_series, use_numpy)
+
+ def test_insert(self, data, request):
+ pa_dtype = data.dtype.pyarrow_dtype
+ tz = getattr(pa_dtype, "tz", None)
+ if pa_version_under2p0 and tz not in (None, "UTC"):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}"
+ )
+ )
+ super().test_insert(data)
+
+ def test_combine_first(self, data, request, using_array_manager):
+ pa_dtype = data.dtype.pyarrow_dtype
+ tz = getattr(pa_dtype, "tz", None)
+ if using_array_manager and pa.types.is_duration(pa_dtype):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason=f"Checking ndim when using arraymanager with {pa_dtype}"
+ )
+ )
+ elif pa_version_under2p0 and tz not in (None, "UTC"):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason=f"Not supported by pyarrow < 2.0 with timestamp type {tz}"
+ )
+ )
+ super().test_combine_first(data)
+
+ @pytest.mark.parametrize("frame", [True, False])
+ @pytest.mark.parametrize(
+ "periods, indices",
+ [(-2, [2, 3, 4, -1, -1]), (0, [0, 1, 2, 3, 4]), (2, [-1, -1, 0, 1, 2])],
+ )
+ def test_container_shift(
+ self, data, frame, periods, indices, request, using_array_manager
+ ):
+ pa_dtype = data.dtype.pyarrow_dtype
+ if (
+ using_array_manager
+ and pa.types.is_duration(pa_dtype)
+ and periods in (-2, 2)
+ ):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason=(
+ f"Checking ndim when using arraymanager with "
+ f"{pa_dtype} and periods={periods}"
+ )
+ )
+ )
+ super().test_container_shift(data, frame, periods, indices)
+
+ @pytest.mark.xfail(
+ reason="result dtype pyarrow[bool] better than expected dtype object"
+ )
+ def test_combine_le(self, data_repeated):
+ super().test_combine_le(data_repeated)
+
+ def test_combine_add(self, data_repeated, request):
+ pa_dtype = next(data_repeated(1)).dtype.pyarrow_dtype
+ if pa.types.is_temporal(pa_dtype):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ raises=TypeError,
+ reason=f"{pa_dtype} cannot be added to {pa_dtype}",
+ )
+ )
+ super().test_combine_add(data_repeated)
+
+ def test_searchsorted(self, data_for_sorting, as_series, request):
+ pa_dtype = data_for_sorting.dtype.pyarrow_dtype
+ if pa.types.is_boolean(pa_dtype):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason=f"{pa_dtype} only has 2 unique possible values",
+ )
+ )
+ super().test_searchsorted(data_for_sorting, as_series)
+
+ def test_where_series(self, data, na_value, as_frame, request, using_array_manager):
+ pa_dtype = data.dtype.pyarrow_dtype
+ if using_array_manager and pa.types.is_duration(pa_dtype):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason=f"Checking ndim when using arraymanager with {pa_dtype}"
+ )
+ )
+ elif pa.types.is_temporal(pa_dtype):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ raises=pa.ArrowNotImplementedError,
+ reason=f"Unsupported cast from double to {pa_dtype}",
+ )
+ )
+ super().test_where_series(data, na_value, as_frame)
+
+
 def test_arrowdtype_construct_from_string_type_with_unsupported_parameters():
  with pytest.raises(NotImplementedError, match="Passing pyarrow type"):
  ArrowDtype.construct_from_string("timestamp[s, tz=UTC][pyarrow]")

pandas/tests/extension/test_string.py

@@ -167,9 +167,7 @@ def test_reduce_series_numeric(self, data, all_numeric_reductions, skipna):
 
 
 class TestMethods(base.BaseMethodsTests):
- @pytest.mark.xfail(reason="returns nullable: GH 44692")
- def test_value_counts_with_normalize(self, data):
- super().test_value_counts_with_normalize(data)
+ pass
 
 
 class TestCasting(base.BaseCastingTests):