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TYP: misc fixes for numpy types 3 #36100

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TYP: misc fixes for numpy types 3 #36100

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2 changes: 2 additions & 0 deletions pandas/_typing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,7 +40,9 @@
# array-like

AnyArrayLike = TypeVar("AnyArrayLike", "ExtensionArray", "Index", "Series", np.ndarray)
AnyArrayLikeUnion = Union["ExtensionArray", "Index", "Series", np.ndarray]
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can you add a comment on when you should use the AnyArryLike TypeVar and when the Union (for readers of this code!)

ArrayLike = TypeVar("ArrayLike", "ExtensionArray", np.ndarray)
ArrayLikeUnion = Union["ExtensionArray", np.ndarray]

# scalars

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4 changes: 2 additions & 2 deletions pandas/core/reshape/merge.py
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Expand Up @@ -13,7 +13,7 @@

from pandas._libs import Timedelta, hashtable as libhashtable, lib
import pandas._libs.join as libjoin
from pandas._typing import ArrayLike, FrameOrSeries
from pandas._typing import ArrayLikeUnion, FrameOrSeries
from pandas.errors import MergeError
from pandas.util._decorators import Appender, Substitution

Expand Down Expand Up @@ -1869,7 +1869,7 @@ def _right_outer_join(x, y, max_groups):


def _factorize_keys(
lk: ArrayLike, rk: ArrayLike, sort: bool = True, how: str = "inner"
lk: ArrayLikeUnion, rk: ArrayLikeUnion, sort: bool = True, how: str = "inner"
) -> Tuple[np.array, np.array, int]:
"""
Encode left and right keys as enumerated types.
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4 changes: 2 additions & 2 deletions pandas/io/pytables.py
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Expand Up @@ -16,7 +16,7 @@

from pandas._libs import lib, writers as libwriters
from pandas._libs.tslibs import timezones
from pandas._typing import ArrayLike, FrameOrSeries, Label
from pandas._typing import AnyArrayLikeUnion, ArrayLike, FrameOrSeries, Label
from pandas.compat._optional import import_optional_dependency
from pandas.compat.pickle_compat import patch_pickle
from pandas.errors import PerformanceWarning
Expand Down Expand Up @@ -5076,7 +5076,7 @@ def _dtype_to_kind(dtype_str: str) -> str:
return kind


def _get_data_and_dtype_name(data: ArrayLike):
def _get_data_and_dtype_name(data: AnyArrayLikeUnion):
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might be better to ensure we unwrap to avoid Index/Series?

for my edification: i was under the impression that AnyArrayLike vs AnyArrayLikeUnion only mattered when we had more than one of them in a signature; i.e.

def foo(arg: AnyArrayLike) -> AnyArrayLike:

implies the return type matches the arg type, while using AnyArrayLikeUnion does not imply that. if that's correct, does it matter which we use in cases like this with only one annotation in the signature?

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in this case we change the type of data in the body. i.e assign to data and hence no preserving type of data.

I've used ..Union to be consistent with FrameOrSeries.

we could change ArrayLike and AnyArrayLike to be unions and ArrayLikeT and AnyArrayLikeT for type variables.

or we could assign to another variable name in the body (simpler, but imo messy and less clear)

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im happy to follow your lead on this

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@WillAyd wdyt?

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or we could assign to another variable name in the body (simpler, but imo messy and less clear)

I don't think worth going down this path. We will likely need a Union type for variable type annotations anyway, since if we use ArrayLike we would get error: Type variable "pandas._typing.ArrayLike" is unbound [valid-type]

"""
Convert the passed data into a storable form and a dtype string.
"""
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