TYP: overload lib.maybe_convert_objects #41166
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pandas/core/strings/object_array.py
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| # error: Argument 1 to "ndarray" has incompatible type "int"; | ||
| # expected "Sequence[int]" | ||
| return np.ndarray(0, dtype=dtype) # type: ignore[arg-type] | ||
| # error: Argument "dtype" to "array" has incompatible type | ||
| # "Union[ExtensionDtype, str, dtype[Any], Type[object]]"; expected | ||
| # "Union[dtype[Any], None, type, _SupportsDType, str, | ||
| # Union[Tuple[Any, int], Tuple[Any, Union[int, Sequence[int]]], List[Any], | ||
| # _DTypeDict, Tuple[Any, Any]]]" | ||
| return np.array([], dtype=dtype) # type: ignore[arg-type] |
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I'm confused about why mypy reports this, if I try making a file t.py with
import numpy as np
from pandas._typing import Dtype, Optional
def foo(dtype: Optional[Dtype] = None):
if dtype is None:
dtype = np.dtype("object")
return (np.array([], dtype=type))then mypy type checks it just fine. Do you know what I might be missing?
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no idea. i think this edit is unrelated to most of the rest; using np.ndarray here instead of np.array weirds me out
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The Dtype alias includes ExtensionDtype which cannot be passed on to numpy
>>> np.array([], dtype=pd.Int64Dtype())
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: Cannot interpret 'Int64Dtype()' as a data type
>>>
Do you know what I might be missing?
return (np.array([], dtype=type)) -> return (np.array([], dtype=dtype))
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so this looks like there maybe an actual bug here. As part of the ArrowStringArray work, I have been parameterising existing tests on object dtype arrays with StringArray and ArrowStringArray and this has maybe uncovered some latent bugs with StringArray.
I think OK to leave this ignore as a 'fix later' and out of scope here.
| new_values, _ = func( | ||
| self.to_numpy(object), # type: ignore[arg-type] | ||
| self.to_numpy("object"), |
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This is an unrelated change to the scope (from the PR title) of this PR. The mypy error is a false positive, no need to change code. will be fixed in #41185
but I guess nbd (other than merge conflicts)
pandas/core/strings/object_array.py
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| # error: Argument 1 to "ndarray" has incompatible type "int"; | ||
| # expected "Sequence[int]" | ||
| return np.ndarray(0, dtype=dtype) # type: ignore[arg-type] | ||
| # error: Argument "dtype" to "array" has incompatible type | ||
| # "Union[ExtensionDtype, str, dtype[Any], Type[object]]"; expected | ||
| # "Union[dtype[Any], None, type, _SupportsDType, str, | ||
| # Union[Tuple[Any, int], Tuple[Any, Union[int, Sequence[int]]], List[Any], | ||
| # _DTypeDict, Tuple[Any, Any]]]" | ||
| return np.array([], dtype=dtype) # type: ignore[arg-type] |
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so this looks like there maybe an actual bug here. As part of the ArrowStringArray work, I have been parameterising existing tests on object dtype arrays with StringArray and ArrowStringArray and this has maybe uncovered some latent bugs with StringArray.
I think OK to leave this ignore as a 'fix later' and out of scope here.
pandas/core/tools/datetimes.py
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| # error: Incompatible types in assignment (expression has type "None", variable has | ||
| # type "ExtensionArray") | ||
| result = None # type: ignore[assignment] | ||
| result = None |
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is this needed? The if result is None will always be True?
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