Allow None when nan_as_null=False in column constructor - #15709
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None when nan_as_null=FalseNone when nan_as_null=False in column constructor
galipremsagar
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May 9, 2024
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| elif nan_as_null is False and ( | ||
| pd.isna(arbitrary).any() | ||
| and inferred_dtype not in ("decimal", "empty") | ||
| and inferred_dtype not in ("decimal", "empty", "string") | ||
| ): | ||
| # Decimal can hold float("nan") |
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@mroeschke After the changes in this PR, do you think this block might be redundant or does it still capture some error scenarios?
bdice
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May 9, 2024
Co-authored-by: Bradley Dice <bdice@bradleydice.com>
Co-authored-by: Bradley Dice <bdice@bradleydice.com>
bdice
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May 9, 2024
mroeschke
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May 9, 2024
galipremsagar
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May 10, 2024
mroeschke
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May 10, 2024
mroeschke
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May 10, 2024
| pd.isna(arbitrary).any() | ||
| any( | ||
| (isinstance(x, (np.floating, float)) and np.isnan(x)) | ||
| or (inferred_dtype == "boolean" and pd.isna(arbitrary)) |
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Would be good to have (inferred_dtype == "boolean" and pd.isna(arbitrary)) be evaluated outside the loop.
Also what case is this condition trying to catch?
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Done.
Also what case is this condition trying to catch?
It is trying to catch this case:
pd.Series(["a", "b", np.nan], dtype='object')
mroeschke
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May 14, 2024
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bdice
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| f"Cannot have mixed values with {inferred_dtype}" | ||
| ) | ||
| elif ( | ||
| nan_as_null is False |
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Don't compare to booleans with is.
Suggested change
| nan_as_null is False | |
| not nan_as_null |
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| raise MixedTypeError( | ||
| f"Cannot have mixed values with {inferred_dtype}" | ||
| ) | ||
| elif nan_as_null is False and _has_any_nan(arbitrary): |
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Suggested change
| elif nan_as_null is False and _has_any_nan(arbitrary): | |
| elif not nan_as_null and _has_any_nan(arbitrary): |
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We need this comparison because None is also a supported parameter and it is similar to True behavior.
Co-authored-by: Bradley Dice <bdice@bradleydice.com>
bdice
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May 14, 2024
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|
/merge |
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Description
Fixes: #15708
This PR fixes an issue where we were throwing an error when
Noneis present andnan_as_null=False, this is a bug because of usingpd.isna, this returnsTruefornan,NoneandNA. Whereas we are only looking fornp.nanand notNoneandpd.NAChecklist