8000 BUG: Handle zero-chunked pyarrow.ChunkedArray in StringArray by xhochy · Pull Request #41052 · pandas-dev/pandas · GitHub
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BUG: Handle zero-chunked pyarrow.ChunkedArray in StringArray #41052

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Merged
merged 5 commits into from
Apr 21, 2021
Merged

BUG: Handle zero-chunked pyarrow.ChunkedArray in StringArray #41052

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3dab96d
BUG: Handle zero-chunked pyarrow.ChunkedArray in StringArray
xhochy Apr 20, 2021
e97ad97
Apply the same fix for masked types
xhochy Apr 20, 2021
f4a2c8c
Use `not results`
xhochy Apr 20, 2021
58736a6
Update doc/source/whatsnew/v1.3.0.rst
xhochy Apr 21, 2021
5ce19cf
Fix IntervalArray and PeriodArray
xhochy Apr 21, 2021
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BUG: Handle zero-chunked pyarrow.ChunkedArray in StringArray
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@xhochy
xhochy committed Apr 20, 2021
commit 3dab96d87f3eeba1b710e09624ff9e3730121f07
2 changes: 1 addition & 1 deletion doc/source/whatsnew/v1.3.0.rst
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Expand Up @@ -700,7 +700,7 @@ Conversion
Strings
^^^^^^^

-
< 8000 span class='blob-code-inner blob-code-marker ' data-code-marker="+">- Bug in the conversion from ``pyarrow.ChunkedArray`` to :class:`StringArray` when the original had zero chunks (:issue:`41040`)
-

Interval
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5 changes: 4 additions & 1 deletion pandas/core/arrays/string_.py
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8000
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Expand Up @@ -118,7 +118,10 @@ def __from_arrow__(
str_arr = StringArray._from_sequence(np.array(arr))
results.append(str_arr)

return StringArray._concat_same_type(results)
if len(results) > 0:
return StringArray._concat_same_type(results)
else:
return StringArray(np.array([], dtype="object"))


class StringArray(PandasArray):
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16 changes: 16 additions & 0 deletions pandas/tests/arrays/string_/test_string.py
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Expand Up @@ -476,6 +476,22 @@ def test_arrow_roundtrip(dtype, dtype_object):
assert result.loc[2, "a"] is pd.NA


@td.skip_if_no("pyarrow", min_version="0.15.1.dev")
def test_arrow_load_from_zero_chunks(dtype, dtype_object):
# GH-41040
import pyarrow as pa

data = pd.array([], dtype=dtype)
df = pd.DataFrame({"a": data})
table = pa.table(df)
assert table.field("a").type == "string"
# Instantiate the same table with no chunks at all
table = pa.table([pa.chunked_array([], type=pa.string())], schema=table.schema)
result = table.to_pandas()
assert isinstance(result["a"].dtype, dtype_object)
tm.assert_frame_equal(result, df)


def test_value_counts_na(dtype):
arr = pd.array(["a", "b", "a", pd.NA], dtype=dtype)
result = arr.value_counts(dropna=False)
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