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Merged
merged 6 commits into from
Jun 2, 2023
Merged

add proper type when grouping by a Series #708

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ac92a2f
remove Series from GroupByObjectNonScalar, add new orverloads for Ser…
ClementPinard May 24, 2023
413f08d
Add tests for iteration over groupby
ClementPinard May 24, 2023
4eeb52a
Add dtype in tests
ClementPinard May 24, 2023
b984743
address PR comments
ClementPinard May 30, 2023
903b662
Merge branch 'pandas-dev:main' into feature/groupby_series
ClementPinard Jun 1, 2023
8d692fb
Use bound method for ByT and SeriesByT typevars
ClementPinard Jun 1, 2023
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22 changes: 20 additions & 2 deletions pandas-stubs/_typing.pyi
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Original file line number Diff line number Diff line change
Expand Up @@ -427,12 +427,30 @@ ByT = TypeVar(
Interval[Timedelta],
tuple,
)
# Use a distinct SeriesByT when using groupby with Series of known dtype.
# Essentially, an intersection between Series S1 TypeVar, and ByT TypeVar
SeriesByT = TypeVar(
"SeriesByT",
str,
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We've been recently advised to no longer use constrained TypeVars, so can you change this to SeriesByT = TypeVar("SeriesByT", bound=str | bytes | ...) We made the change for S1 in a recent PR

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Hi,

What is the difference between TypeVar("SeriesByT", bound = str | bytes ...) and TypeVar("SeriesByT, str, byts, ...) ?

I tried to replace the SeriesByT with the bound keyword, but then the mypy did not pass anymore :

Poe => mypy

===========================================
Beginning: 'Run mypy on 'tests' (using the local stubs) and on the local stubs'
===========================================

pandas-stubs/core/series.pyi:648: error: Type variable "SeriesByT" not valid as type argument value for "SeriesGroupBy"  [type-var]
pandas-stubs/core/frame.pyi:1100: error: Type variable "SeriesByT" not valid as type argument value for "DataFrameGroupBy"  [type-var]
Found 2 errors in 2 files (checked 224 source files)

===========================================
Step: 'Run mypy on 'tests' (using the local stubs) and on the local stubs' failed!
===========================================

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bound allows the type to be a union of all the listed types. Without it, the type has to be one of the listed (sub-)types.

You probably also need to adjust the type used for SeriesGroupBy and DataFrameGroupBy to use bound.

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Thanks I actually tried that and got rid of the errors.

bytes,
datetime.date,
bool,
int,
float,
complex,
Timestamp,
Timedelta,
Period,
Interval[int],
Interval[float],
Interval[Timestamp],
Interval[Timedelta],
)
GroupByObjectNonScalar: TypeAlias = (
tuple
| list[_HashableTa]
| Function
| list[Function]
| Series
| list[Series]
| np.ndarray
| list[np.ndarray]
Expand All @@ -442,7 +460,7 @@ GroupByObjectNonScalar: TypeAlias = (
| Grouper
| list[Grouper]
)
GroupByObject: TypeAlias = Scalar | Index | GroupByObjectNonScalar
GroupByObject: TypeAlias = Scalar | Index | GroupByObjectNonScalar | Series

StataDateFormat: TypeAlias = Literal[
"tc",
Expand Down
16 changes: 15 additions & 1 deletion pandas-stubs/core/frame.pyi
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Expand Up @@ -106,6 +106,7 @@ from pandas._typing import (
ReplaceMethod,
Scalar,
ScalarT,
SeriesByT,
SortKind,
StataDateFormat,
StorageOptions,
Expand Down Expand Up @@ -1085,7 +1086,20 @@ class DataFrame(NDFrame, OpsMixin):
@overload
def groupby(
self,
by: CategoricalIndex | Index,
by: Series[SeriesByT],
axis: Axis = ...,
level: Level | None = ...,
as_index: _bool = ...,
sort: _bool = ...,
group_keys: _bool = ...,
squeeze: _bool = ...,
observed: _bool = ...,
dropna: _bool = ...,
) -> DataFrameGroupBy[SeriesByT]: ...
@overload
def groupby(
self,
by: CategoricalIndex | Index | Series,
axis: Axis = ...,
level: Level | None = ...,
as_index: _bool = ...,
Expand Down
16 changes: 15 additions & 1 deletion pandas-stubs/core/series.pyi
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Expand Up @@ -129,6 +129,7 @@ from pandas._typing import (
Renamer,
ReplaceMethod,
Scalar,
SeriesByT,
SortKind,
StrDtypeArg,
TimedeltaDtypeArg,
Expand Down Expand Up @@ -639,7 +640,20 @@ class Series(IndexOpsMixin, NDFrame, Generic[S1]):
@overload
def groupby(
self,
by: CategoricalIndex | Index,
by: Series[SeriesByT],
axis: AxisIndex = ...,
level: Level | None = ...,
as_index: _bool = ...,
sort: _bool = ...,
group_keys: _bool = ...,
squeeze: _bool = ...,
observed: _bool = ...,
dropna: _bool = ...,
) -> SeriesGroupBy[S1, SeriesByT]: ...
@overload
def groupby(
self,
by: CategoricalIndex | Index | Series,
axis: AxisIndex = ...,
level: Level | None = ...,
as_index: _bool = ...,
Expand Down
14 changes: 14 additions & 0 deletions tests/test_frame.py
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Expand Up @@ -985,6 +985,20 @@ def test_types_groupby_any() -> None:
)


def test_types_groupby_iter() -> None:
df = pd.DataFrame(data={"col1": [1, 1, 2], "col2": [3, 4, 5]})
series_groupby = pd.Series([True, True, False], dtype=bool)
first_group = next(iter(df.groupby(series_groupby)))
check(
assert_type(first_group[0], bool),
bool,
)
check(
assert_type(first_group[1], pd.DataFrame),
pd.DataFrame,
)


def test_types_merge() -> None:
df = pd.DataFrame(data={"col1": [1, 1, 2], "col2": [3, 4, 5]})
df2 = pd.DataFrame(data={"col1": [1, 1, 2], "col2": [0, 1, 0]})
Expand Down
11 changes: 11 additions & 0 deletions tests/test_series.py
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Expand Up @@ -731,6 +731,17 @@ def test_types_group_by_with_dropna_keyword() -> None:
s.groupby(level=0).sum()


def test_types_groupby_iter() -> None:
s = pd.Series([1, 1, 2], dtype=int)
series_groupby = pd.Series([True, True, False], dtype=bool)
first_group = next(iter(s.groupby(series_groupby)))
check(
assert_type(first_group[0], bool),
bool,
)
check(assert_type(first_group[1], "pd.Series[int]"), pd.Series, np.integer)


def test_types_plot() -> None:
s = pd.Series([0, 1, 1, 0, -10])
if TYPE_CHECKING: # skip pytest
Expand Down