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Series(Mapping) #843

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merged 6 commits into from
Dec 26, 2023
Merged

Series(Mapping) #843

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2862b3a
Series(Mapping)
twoertwein Dec 24, 2023
2f50dc4
Hashable keys
twoertwein Dec 24, 2023
8289218
empty
twoertwein Dec 25, 2023
59234bf
incremental changes
twoertwein Dec 26, 2023
3c92612
Merge remote-tracking branch 'upstream/main' into series_mapping
twoertwein Dec 26, 2023
ef2ea63
_str
twoertwein Dec 26, 2023
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13 changes: 8 additions & 5 deletions pandas-stubs/core/series.pyi
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Original file line number Diff line number Diff line change
Expand Up @@ -228,6 +228,7 @@ class Series(IndexOpsMixin[S1], NDFrame):
cls,
data: DatetimeIndex
| Sequence[np.datetime64 | datetime]
| Mapping[Any, np.datetime64 | datetime]
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| np.datetime64
| datetime,
index: Axes | None = ...,
Expand All @@ -239,7 +240,7 @@ class Series(IndexOpsMixin[S1], NDFrame):
@overload
def __new__( # type: ignore[overload-overlap]
cls,
data: _ListLike,
data: Iterable,
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index: Axes | None = ...,
*,
dtype: TimestampDtypeArg,
Expand All @@ -261,6 +262,7 @@ class Series(IndexOpsMixin[S1], NDFrame):
cls,
data: TimedeltaIndex
| Sequence[np.timedelta64 | timedelta]
| Mapping[Any, np.timedelta64 | timedelta]
| np.timedelta64
| timedelta,
index: Axes | None = ...,
Expand All @@ -274,7 +276,8 @@ class Series(IndexOpsMixin[S1], NDFrame):
cls,
data: IntervalIndex[Interval[_OrderableT]]
| Interval[_OrderableT]
| Sequence[Interval[_OrderableT]],
| Sequence[Interval[_OrderableT]]
| Mapping[Any, Interval[_OrderableT]],
index: Axes | None = ...,
*,
dtype: Literal["Interval"] = ...,
Expand All @@ -284,7 +287,7 @@ class Series(IndexOpsMixin[S1], NDFrame):
@overload
def __new__(
cls,
data: Scalar | _ListLike | dict[int, Any] | dict[_str, Any] | None,
data: Scalar | Iterable | None,
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I don't think Iterable is correct here, because we don't (or shouldn't) accept a set.

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Would it be correct to have one Never-overload for set and then simply use data: Any + specified dtype?

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@Dr-Irv Dr-Irv Dec 26, 2023 •
edited by twoertwein
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I don't know. I'd rather keep things narrow by just using Scalar | _ListLike | dict[Hashable, Any] | None . Or maybe we can use Mapping[Hashable, Scalar] instead of dict[Hashable, Any] .

My feeling is that we should make small increments when we change the stubs so as to not widen things too much.

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My feeling is that we should make small increments when we change the stubs so as to not widen things too much.

[sorry, I edited your comment by accident - wanted to quote reply]

I agree about that! But I also feel like we are making ourselves too much work by trying to be as restrictive as possible.

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I agree about that! But I also feel like we are making ourselves too much work by trying to be as restrictive as possible.

Possibly. But I'd rather let the community report issues, and we really haven't had too many issues where we made things too restrictive.

See what I wrote here 18 months ago about this topic: https://github.com/pandas-dev/pandas-stubs/blob/main/docs/philosophy.md#narrow-vs-wide-arguments

index: Axes | None = ...,
*,
dtype: type[S1],
Expand All @@ -294,7 +297,7 @@ class Series(IndexOpsMixin[S1], NDFrame):
@overload
def __new__(
cls,
data: S1 | _ListLike[S1] | dict[int, S1] | dict[_str, S1],
data: S1 | Mapping[Any, S1] | _ListLike[S1],
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I'm not happy with _ListLike but a Mapping is also Iterable.

index: Axes | None = ...,
*,
dtype: Dtype = ...,
Expand All @@ -304,7 +307,7 @@ class Series(IndexOpsMixin[S1], NDFrame):
@overload
def __new__(
cls,
data: Scalar | _ListLike | dict[int, Any] | dict[_str, Any] | None = ...,
data: Scalar | Iterable | None = ...,
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same comment as above about Iterable

index: Axes | None = ...,
*,
dtype: Dtype = ...,
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29 changes: 29 additions & 0 deletions tests/test_series.py
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Expand Up @@ -2860,3 +2860,32 @@ def test_round() -> None:
def test_series_new_empty() -> None:
# GH 826
check(assert_type(pd.Series(), "pd.Series[Any]"), pd.Series)


def test_series_mapping() -> None:
# GH 831
check(
assert_type(
pd.Series(
{
pd.Timestamp(2023, 1, 2): "b",
}
),
"pd.Series[str]",
),
pd.Series,
str,
)

check(
assert_type(
pd.Series(
{
("a", "b"): "c",
}
),
"pd.Series[str]",
),
pd.Series,
str,
)