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[ArrowStringArray] fix test_value_counts_na #41002

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Merged
merged 1 commit into from
Apr 19, 2021
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[ArrowStringArray] fix test_value_counts_na #41002

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15 changes: 10 additions & 5 deletions pandas/core/arrays/string_arrow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -675,13 +675,18 @@ def value_counts(self, dropna: bool = True) -> Series:

vc = self._data.value_counts()

# Index cannot hold ExtensionArrays yet
index = Index(type(self)(vc.field(0)).astype(object))
values = vc.field(0)
counts = vc.field(1)
if dropna and self._data.null_count > 0:
mask = values.is_valid()
values = values.filter(mask)
counts = counts.filter(mask)

# No missing values so we can adhere to the interface and return a numpy array.
counts = np.array(vc.field(1))
counts = np.array(counts)

if dropna and self._data.null_count > 0:
raise NotImplementedError("yo")
# Index cannot hold ExtensionArrays yet
index = Index(type(self)(values)).astype(object)

return Series(counts, index=index).astype("Int64")

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14 changes: 2 additions & 12 deletions pandas/tests/arrays/string_/test_string.py
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Expand Up @@ -476,12 +476,7 @@ def test_arrow_roundtrip(dtype, dtype_object):
assert result.loc[2, "a"] is pd.NA


def test_value_counts_na(dtype, request):
if dtype == "arrow_string":
reason = "TypeError: boolean value of NA is ambiguous"
mark = pytest.mark.xfail(reason=reason)
request.node.add_marker(mark)

def test_value_counts_na(dtype):
arr = pd.array(["a", "b", "a", pd.NA], dtype=dtype)
result = arr.value_counts(dropna=False)
expected = pd.Series([2, 1, 1], index=["a", "b", pd.NA], dtype="Int64")
Expand All @@ -492,12 +487,7 @@ def test_value_counts_na(dtype, request):
tm.assert_series_equal(result, expected)


def test_value_counts_with_normalize(dtype, request):
if dtype == "arrow_string":
reason = "TypeError: boolean value of NA is ambiguous"
mark = pytest.mark.xfail(reason=reason)
request.node.add_marker(mark)

def test_value_counts_with_normalize(dtype):
s = pd.Series(["a", "b", "a", pd.NA], dtype=dtype)
result = s.value_counts(normalize=True)
expected = pd.Series([2, 1], index=["a", "b"], dtype="Float64") / 3
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