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BUG: Impossible creation of array with dtype=string #61263
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if isinstance(scalars, list) and all(isinstance(x, list) for x in scalars): | ||
scalars = [str(x) for x in scalars] |
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Instead of this, can you modify ensure_string_array
in pandas._libs.lib.pyx
as follows. Instead of
elif not util.is_array(arr):
arr = np.array(arr, dtype="object")
do
elif not util.is_array(arr):
# GH#61155: Guarantee a 1-d result when array is a list of lists
arr = np.empty(len(array), dtype="object")
arr[:] = array
Will has almost no performance impact.
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Thank you very much for the suggestions, I have made the necessary changes as per the guidance.
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Thanks for making the update. The changes in this file should now be reverted.
Also, please add a test for this. |
pre-commit.ci autofix |
for more information, see https://pre-commit.ci
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We use pytest for testing, you'll need to add a test using that format. See here:
https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#using-pytest
The general pytest introduction may also be useful:
Thank you for the details, will work on it |
closes #61155
Hello @rhshadrach ,
I’ve created a fix that raises a ValueError when trying to create a StringArray from a list of lists with inconsistent lengths or non-character elements. This aligns the behavior for both consistent and inconsistent input formats and also tested.
I've would like to hear opinion to raise an error when a list of lists is passed for
dtype=StringDtype
, to avoid ambiguous behavior. If preferred, we could instead join the inner lists into strings automatically — happy to adjust based on guidance.Example case :
pd.array([["t", "e", "s", "t"], ["w", "o", "r", "d"]], dtype="string")
output : <StringArray> ['test', 'word'] Length: 2, dtype: string
Thanks