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clib.conversion._to_numpy: Add tests for pandas.Series with pandas string dtype #3607
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@@ -1475,7 +1475,7 @@ def virtualfile_from_vectors( | |||
# 2 columns contains coordinates like longitude, latitude, or datetime string | |||
# types. | |||
for col, array in enumerate(arrays[2:]): | |||
if pd.api.types.is_string_dtype(array.dtype): | |||
if np.issubdtype(array.dtype, np.str_): |
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@@ -1506,9 +1506,9 @@ def virtualfile_from_vectors( | |||
strings = string_arrays[0] | |||
elif len(string_arrays) > 1: | |||
strings = np.array( | |||
[" ".join(vals) for vals in zip(*string_arrays, strict=True)] | |||
[" ".join(vals) for vals in zip(*string_arrays, strict=True)], | |||
dtype=np.str_, |
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Specifying dtype
is not necesary here, but I feel it's good to expicitly tell that here we're expecting a np.str_ array.
@@ -175,6 +179,11 @@ def _to_numpy(data: Any) -> np.ndarray: | |||
else: | |||
vec_dtype = str(getattr(data, "dtype", "")) | |||
array = np.ascontiguousarray(data, dtype=dtypes.get(vec_dtype)) | |||
|
|||
# Check if a np.object_ array can be converted to np.str_. |
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This is necessary to support pd.Series string like:
x = pd.Series(["abc", "defg", "12345"], dtype=None)
x = pd.Series(["abc", "defg", "12345"], dtype=np.str_)
x = pd.Series(["abc", "defg", "12345"], dtype="U10")
) | ||
strings = np.asanyarray(a=strings, dtype=np.str_) |
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Description of proposed changes
Add tests for pandas.Series with string dtype. Six cases are tested:
dtype=None
dtype=np.str_
dtype="U10"
dtype="string[python]"
dtype="string[pyarrow]"
dtype="string[pyarrow_numpy]"
Neither can be converted to
np.str_
directly. Cases 4-6 can be fixed by 01ba317, and cases 1-3 can be fixed by dac7e8e.