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added return types for "SeriesGroupBy" and "DataFrameGroupBy" #455

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Merged
merged 4 commits into from
Nov 30, 2022
Merged

added return types for "SeriesGroupBy" and "DataFrameGroupBy" #455

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8c808ad
added return types for "SeriesGroupBy" and "DataFrameGroupBy" with tests
ramvikrams Nov 29, 2022
8ca985c
added function callable in transform
ramvikrams Nov 29, 2022
f4b8047
added 'str' to ' func' in transform
ramvikrams Nov 29, 2022
d695875
added the tests with a string for transform
ramvikrams Nov 30, 2022
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4 changes: 2 additions & 2 deletions pandas-stubs/core/groupby/generic.pyi
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Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,7 @@ class SeriesGroupBy(GroupBy, Generic[S1]):
def agg(self, func: list[AggFuncTypeBase], *args, **kwargs) -> DataFrame: ...
@overload
def agg(self, func: AggFuncTypeBase, *args, **kwargs) -> Series: ...
def transform(self, func, *args, **kwargs): ...
def transform(self, func: Callable | str, *args, **kwargs) -> Series: ...
def filter(self, func, dropna: bool = ..., *args, **kwargs): ...
def nunique(self, dropna: bool = ...) -> Series: ...
def describe(self, **kwargs) -> DataFrame: ...
Expand Down Expand Up @@ -166,7 +166,7 @@ class DataFrameGroupBy(GroupBy):
) -> DataFrame: ...
def aggregate(self, arg: AggFuncTypeFrame = ..., *args, **kwargs) -> DataFrame: ...
agg = aggregate
def transform(self, func, *args, **kwargs): ...
def transform(self, func: Callable | str, *args, **kwargs) -> DataFrame: ...
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Can/should we make this a Literal?

From https://github.com/pandas-dev/pandas/blob/main/pandas/core/groupby/base.py#L60, the transformation kernels would be the ones applicable here:

transformation_kernels = Literal[
    "bfill",
    "cumcount",
    "cummax",
    "cummin",
    "cumprod",
    "cumsum",
    "diff",
    "ffill",
    "fillna",
    "ngroup",
    "pct_change",
    "rank",
    "shift",
]

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Is there any way to do better than Callable? Like what about Callable[Concatenate[pd.DataFrame, P], pd.DataFrame]? Will ParamSpec work in that context? If not, can we use a Protocol? I know that we can't use P.args on *args and P.kwargs on **kwargs, though that would have been a very nice feature.

I'm just thinking, if we are going from an unannotated function to an annotated function, a loose annotation doesn't really do that much good versus not having an annotation at all.

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@ramvikrams ramvikrams Nov 30, 2022 •
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we can do this , but I think it would be better to ask Dr Irv about adding it.

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I'd rather be incremental in terms of PR's where we could specify the Literal values (there are more than just the ones you listed), as well as making the func argument more specific. @gandhis1 feel free to create an issue indicating that we could improve the typing on groupby.transform by being more specific about the allowed argument combinations. It also would require a lot more tests.

Focus of this PR was just to get the return types right, and do a slight improvement in the func argument.

def filter(
self, func: Callable, dropna: bool = ..., *args, **kwargs
) -> DataFrame: ...
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26 changes: 26 additions & 0 deletions tests/test_frame.py
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Expand Up @@ -2063,3 +2063,29 @@ def test_setitem_none() -> None:
sb = pd.Series([1, 2, 3], dtype=int)
sb.loc["y"] = None
sb.iloc[0] = None


def test_groupby_and_transform() -> None:
df = pd.DataFrame(
{
"A": ["foo", "bar", "foo", "bar", "foo", "bar"],
"B": ["one", "one", "two", "three", "two", "two"],
"C": [1, 5, 5, 2, 5, 5],
"D": [2.0, 5.0, 8.0, 1.0, 2.0, 9.0],
}
)
ser = pd.Series(
[390.0, 350.0, 30.0, 20.0],
index=["Falcon", "Falcon", "Parrot", "Parrot"],
name="Max Speed",
)
grouped = df.groupby("A")[["C", "D"]]
grouped1 = ser.groupby(ser > 100)
c1 = grouped.transform("sum")
c2 = grouped.transform(lambda x: (x - x.mean()) / x.std())
c3 = grouped1.transform("cumsum")
c4 = grouped1.transform(lambda x: x.max() - x.min())
check(assert_type(c1, pd.DataFrame), pd.DataFrame)
check(assert_type(c2, pd.DataFrame), pd.DataFrame)
check(assert_type(c3, pd.Series), pd.Series)
check(assert_type(c4, pd.Series), pd.Series)