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DOC/TYP: index.take return val #40521

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Merged
merged 15 commits into from
May 23, 2021
Merged

DOC/TYP: index.take return val #40521

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DOC/TYP: index.take
mzeitlin11 Mar 19, 2021
67b6299
Fix default
mzeitlin11 Mar 19, 2021
b609767
Update pandas/core/indexes/base.py
mzeitlin11 Mar 22, 2021
8b917de
Use AnyArrayLike instead of ArrayLike
mzeitlin11 Mar 22, 2021
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Merge branch 'doc/index_take_meth' of github.com:/mzeitlin11/pandas i…
mzeitlin11 Mar 22, 2021
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mzeitlin11 Mar 31, 2021
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Merge remote-tracking branch 'origin/master' into doc/index_take_meth
mzeitlin11 Apr 2, 2021
c37be8c
Remove complicated typing, doc fixes
mzeitlin11 Apr 2, 2021
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mzeitlin11 Apr 6, 2021
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mzeitlin11 Apr 8, 2021
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24 changes: 17 additions & 7 deletions pandas/core/indexes/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -887,7 +887,7 @@ def astype(self, dtype, copy=True):

Parameters
----------
indices : list
indices : array-like
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i think Sequence[int] would be more helpful

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Happy to change, my only question would be if we want to be using type hints for user facing API. The numpy documentation for take just uses array_like

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i think Sequence[int] would be more helpful

we shouldn't use typing notation for docstrings. https://pandas.pydata.org/pandas-docs/dev/development/contributing_docstring.html#parameter-types

and from that guide...

If the exact type is not relevant, but must be compatible with a NumPy array, array-like can be specified. If Any type that can be iterated is accepted, iterable can be used:

but the guide doesn't mention sequence.

maybe sequence of int is allowed, but I think matching numpy is fine.

Indices to be taken.
axis : int, optional
The axis over which to select values, always 0.
Expand All @@ -898,8 +898,8 @@ def astype(self, dtype, copy=True):

Returns
-------
numpy.ndarray
Elements of given indices.
Index
An index formed of elements at the given indices.

See Also
--------
Expand All @@ -908,16 +908,26 @@ def astype(self, dtype, copy=True):
"""

@Appender(_index_shared_docs["take"] % _index_doc_kwargs)
def take(self, indices, axis=0, allow_fill=True, fill_value=None, **kwargs):
def take(
self,
indices: Union[AnyArrayLike, Sequence[int]],
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isnt Sequence[int] going to cover the AnyArrayLike case?

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I think even with AnyArrayLike in place of ArrayLike we run into the issue mentioned here #40521 (comment)

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this is too complicated and specific an annotation to put here, leave this untyped for now, or put it in a typing alias, but then we should enforce it, alt we shouldn't allow non-array-likes here

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Left untyped for now, now mostly just a doc fix

axis: int = 0,
allow_fill: bool = True,
fill_value=None,
**kwargs,
):
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Any advice on typing this return value? Can almost use the TypeVar _IndexT, but the problem is that RangeIndex.take returns a superclass (Int64Index).

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I think in the base class use Index, and can then use a typevar in the subclasses that do return the same type.

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I tried something along those lines originally - the problem I was running into is how to handle subclasses which rely on parent implementations. For example, for ExtensionIndex would like to be able to write a stub like

def take(
        self: _ExtensionIndexT,
        indices,
        axis: int = 0,
        allow_fill: bool = True,
        fill_value=None,
        **kwargs,
    ) -> _ExtensionIndexT: ...

to essentially type the child while still relying on the inherited implementation. But couldn't figure out a way to achieve this.

if kwargs:
nv.validate_take((), kwargs)
indices = ensure_platform_int(indices)
allow_fill = self._maybe_disallow_fill(allow_fill, fill_value, indices)
indices_as_array = ensure_platform_int(indices)
allow_fill = self._maybe_disallow_fill(allow_fill, fill_value, indices_as_array)

# Note: we discard fill_value and use self._na_value, only relevant
# in the case where allow_fill is True and fill_value is not None
taken = algos.take(
self._values, indices, allow_fill=allow_fill, fill_value=self._na_value
self._values,
indices_as_array,
allow_fill=allow_fill,
fill_value=self._na_value,
)
return type(self)._simple_new(taken, name=self.name)

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