TYP: optional type parameters for ndarray and flatiter
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jorenham
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The new The The remaining errors appear to be a consequence of mypy's type-inference limitations in case of redefinitions (which I believe will no longer be an issue in the upcoming mypy 1.16 release, which has been branched already) |
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@mroeschke, any thoughts on this? |
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Thanks for the ping again. Happy to address the pandas errors during release. cc @Dr-Irv for the pandas-stubs errors |
Same for |
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@jorenham I've created a PR in |
Good! Mypy primer runs on the main branch, so once it's merged rerunning CI should do the trick 👌 |
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Diff from mypy_primer, showing the effect of this PR on type check results on a corpus of open source code: xarray (https://github.com/pydata/xarray)
+ xarray/tests/test_parallelcompat.py: note: In member "compute" of class "DummyChunkManager":
+ xarray/tests/test_parallelcompat.py:93: error: Return type "tuple[ndarray[tuple[int, ...], dtype[Any]], ...]" of "compute" incompatible with return type "tuple[ndarray[Any, _DType_co], ...]" in supertype "ChunkManagerEntrypoint" [override]
+ xarray/tests/test_parallelcompat.py: note: At top level:
dedupe (https://github.com/dedupeio/dedupe)
- dedupe/api.py:551: error: Unused "type: ignore" comment [unused-ignore]
optuna (https://github.com/optuna/optuna)
+ optuna/_hypervolume/hssp.py:108: error: Incompatible types in assignment (expression has type "ndarray[tuple[int, ...], dtype[signedinteger[Any]]]", variable has type "ndarray[tuple[int], dtype[signedinteger[Any]]]") [assignment]
+ optuna/_hypervolume/box_decomposition.py:92: error: Incompatible types in assignment (expression has type "ndarray[tuple[int, ...], dtype[Any]]", variable has type "ndarray[tuple[int, int, int], dtype[Any]]") [assignment]
+ tests/hypervolume_tests/test_wfg.py:26: error: Incompatible types in assignment (expression has type "ndarray[tuple[int, ...], dtype[Any]]", variable has type "ndarray[tuple[int, int], dtype[Any]]") [assignment]
pandas (https://github.com/pandas-dev/pandas)
- pandas/core/_numba/executor.py:90: error: Incompatible redefinition (redefinition with type "Callable[[ndarray[Any, Any], ndarray[Any, Any], ndarray[Any, Any], int, VarArg(Any)], Any]", original type "Callable[[ndarray[Any, Any], ndarray[Any, Any], int, int, VarArg(Any)], Any]") [misc]
+ pandas/core/window/numba_.py:238: error: Incompatible types in assignment (expression has type "ndarray[tuple[int, ...], dtype[Any]]", variable has type "ndarray[tuple[int, int], dtype[float64]]") [assignment]
+ pandas/core/_numba/executor.py:90: error: Incompatible redefinition (redefinition with type "Callable[[ndarray[tuple[int, ...], dtype[Any]], ndarray[tuple[int, ...], dtype[Any]], ndarray[tuple[int, ...], dtype[Any]], int, VarArg(Any)], Any]", original type "Callable[[ndarray[tuple[int, ...], dtype[Any]], ndarray[tuple[int, ...], dtype[Any]], int, int, VarArg(Any)], Any]") [misc]
+ pandas/core/util/hashing.py:327: error: Incompatible types in assignment (expression has type "CategoricalDtype", variable has type "dtype[Any]") [assignment]
+ pandas/core/util/hashing.py:328: error: Argument 2 to "_simple_new" of "Categorical" has incompatible type "dtype[Any]"; expected "CategoricalDtype" [arg-type]
+ pandas/core/nanops.py:656: error: Unused "type: ignore" comment [unused-ignore]
+ pandas/core/construction.py:687: error: Incompatible types in assignment (expression has type "ndarray[tuple[int, ...], dtype[Any]]", variable has type "ndarray[tuple[int], dtype[Any]]") [assignment]
+ pandas/core/construction.py:689: error: Incompatible types in assignment (expression has type "ndarray[tuple[int, ...], dtype[Any]]", variable has type "ndarray[tuple[int], dtype[Any]]") [assignment]
+ pandas/core/arrays/_utils.py:48: error: Item "dtype[Any]" of "ExtensionDtype | dtype[Any]" has no attribute "na_value" [union-attr]
+ pandas/core/arrays/_utils.py:56: error: Item "dtype[Any]" of "ExtensionDtype | dtype[Any]" has no attribute "na_value" [union-attr]
+ pandas/core/arrays/datetimes.py:816: error: Argument 1 to "view" of "ndarray" has incompatible type "dtype[datetime64[date | int | None]] | DatetimeTZDtype"; expected "dtype[Any] | _HasDType[dtype[Any]]" [arg-type]
+ pandas/core/arrays/_mixins.py:150: error: Argument "dtype" to "view" of "ndarray" has incompatible type "ExtensionDtype | dtype[Any]"; expected "dtype[Any] | _HasDType[dtype[Any]]" [arg-type]
+ pandas/core/arrays/categorical.py:1858: error: Incompatible types in assignment (expression has type "ndarray[tuple[int, ...], dtype[Any]]", variable has type "ndarray[tuple[int], dtype[signedinteger[Any]]]") [assignment]
+ pandas/io/pytables.py:3306: error: Unused "type: ignore" comment [unused-ignore]
+ pandas/io/pytables.py:3306: error: "ExtensionArray" has no attribute "asi8" [attr-defined]
+ pandas/io/pytables.py:3306: note: Error code "attr-defined" not covered by "type: ignore" comment
+ pandas/io/pytables.py:3312: error: Unused "type: ignore" comment [unused-ignore]
+ pandas/io/pytables.py:3312: error: "ExtensionArray" has no attribute "tz" [attr-defined]
+ pandas/io/pytables.py:3312: note: Error code "attr-defined" not covered by "type: ignore" comment
+ pandas/io/pytables.py:5196: error: Unused "type: ignore" comment [unused-ignore]
+ pandas/core/frame.py:11368: error: Incompatible types in assignment (expression has type "ndarray[tuple[int, ...], dtype[float64]]", variable has type "ndarray[tuple[int, int], dtype[float64]]") [assignment]
+ pandas/core/reshape/merge.py:1737: error: Item "ExtensionArray" of "ExtensionArray | Any" has no attribute "all" [union-attr]
+ pandas/core/reshape/merge.py:1757: error: Item "ExtensionArray" of "ExtensionArray | Any" has no attribute "all" [union-attr]
static-frame (https://github.com/static-frame/static-frame)
- static_frame/core/join.py:98: error: Argument 1 to "nonzero_1d" has incompatible type "numpy.bool[builtins.bool] | ndarray[tuple[int, ...], dtype[numpy.bool[builtins.bool]]]"; expected "ndarray[Any, Any]" [arg-type]
+ static_frame/core/join.py:98: error: Argument 1 to "nonzero_1d" has incompatible type "numpy.bool[builtins.bool] | ndarray[tuple[int, ...], dtype[numpy.bool[builtins.bool]]]"; expected "ndarray[tuple[int, ...], dtype[Any]]" [arg-type]
+ static_frame/core/frame.py:7524: error: Incompatible types in assignment (expression has type "ndarray[tuple[int, ...], dtype[Any]]", variable has type "ndarray[tuple[int], dtype[Any]]") [assignment]
spark (https://github.com/apache/spark)
- python/pyspark/pandas/namespace.py:1139: note: def [IntStrT: (int, str)] read_excel(io: str | PathLike[str] | ReadBuffer[bytes] | ExcelFile | Any | Any | Any | Any, sheet_name: list[IntStrT], *, header: int | Sequence[int] | None = ..., names: MutableSequence[Any] | ndarray[Any, Any] | tuple[Any, ...] | range | None = ..., index_col: int | Sequence[int] | str | None = ..., usecols: str | SequenceNotStr[Hashable] | range | ExtensionArray | ndarray[Any, Any] | Index[Any] | Series[Any] | Callable[[Any], bool] | None = ..., dtype: str | ExtensionDtype | str | dtype[generic[Any]] | type[str] | type[complex] | type[bool] | type[object] | Mapping[str, str | ExtensionDtype | str | dtype[generic[Any]] | type[str] | type[complex] | type[bool] | type[object]] | None = ..., engine: Literal['xlrd', 'openpyxl', 'odf', 'pyxlsb', 'calamine'] | None = ..., converters: Mapping[int | str, Callable[[object], object]] | None = ..., true_values: Iterable[Hashable] | None = ..., false_values: Iterable[Hashable] | None = ..., skiprows: int | Sequence[int] | Callable[[object], bool] | None = ..., nrows: int | None = ..., na_values: Sequence[str] | dict[str | int, Sequence[str]] = ..., keep_default_na: bool = ..., na_filter: bool = ..., verbose: bool = ..., parse_dates: bool | Sequence[int] | Sequence[Sequence[str] | Sequence[int]] | dict[str, Sequence[int] | list[str]] = ..., date_format: dict[Hashable, str] | str | None = ..., thousands: str | None = ..., decimal: str = ..., comment: str | None = ..., skipfooter: int = ..., storage_options: dict[str, Any] | None = ..., dtype_backend: Literal['pyarrow', 'numpy_nullable'] | Literal[_NoDefault.no_default] = ...) -> dict[IntStrT, DataFrame]
+ python/pyspark/pandas/namespace.py:1139: note: def [IntStrT: (int, str)] read_excel(io: str | PathLike[str] | ReadBuffer[bytes] | ExcelFile | Any | Any | Any | Any, sheet_name: list[IntStrT], *, header: int | Sequence[int] | None = ..., names: MutableSequence[Any] | ndarray[tuple[int, ...], dtype[Any]] | tuple[Any, ...] | range | None = ..., index_col: int | Sequence[int] | str | None = ..., usecols: str | SequenceNotStr[Hashable] | range | ExtensionArray | ndarray[Any, Any] | Index[Any] | Series[Any] | Callable[[Any], bool] | None = ..., dtype: str | ExtensionDtype | str | dtype[generic[Any]] | type[str] | type[complex] | type[bool] | type[object] | Mapping[str, str | ExtensionDtype | str | dtype[generic[Any]] | type[str] | type[complex] | type[bool] | type[object]] | None = ..., engine: Literal['xlrd', 'openpyxl', 'odf', 'pyxlsb', 'calamine'] | None = ..., converters: Mapping[int | str, Callable[[object], object]] | None = ..., true_values: Iterable[Hashable] | None = ..., false_values: Iterable[Hashable] | None = ..., skiprows: int | Sequence[int] | Callable[[object], bool] | None = ..., nrows: int | None = ..., na_values: Sequence[str] | dict[str | int, Sequence[str]] = ..., keep_default_na: bool = ..., na_filter: bool = ..., verbose: bool = ..., parse_dates: bool | Sequence[int] | Sequence[Sequence[str] | Sequence[int]] | dict[str, Sequence[int] | list[str]] = ..., date_format: dict[Hashable, str] | str | None = ..., thousands: str | None = ..., decimal: str = ..., comment: str | None = ..., skipfooter: int = ..., storage_options: dict[str, Any] | None = ..., dtype_backend: Literal['pyarrow', 'numpy_nullable'] | Literal[_NoDefault.no_default] = ...) -> dict[IntStrT, DataFrame]
- python/pyspark/pandas/namespace.py:1139: note: def read_excel(io: str | PathLike[str] | ReadBuffer[bytes] | ExcelFile | Any | Any | Any | Any, sheet_name: None, *, header: int | Sequence[int] | None = ..., names: MutableSequence[Any] | ndarray[Any, Any] | tuple[Any, ...] | range | None = ..., index_col: int | Sequence[int] | str | None = ..., usecols: str | SequenceNotStr[Hashable] | range | ExtensionArray | ndarray[Any, Any] | Index[Any] | Series[Any] | Callable[[Any], bool] | None = ..., dtype: str | ExtensionDtype | str | dtype[generic[Any]] | type[str] | type[complex] | type[bool] | type[object] | Mapping[str, str | ExtensionDtype | str | dtype[generic[Any]] | type[str] | type[complex] | type[bool] | type[object]] | None = ..., engine: Literal['xlrd', 'openpyxl', 'odf', 'pyxlsb', 'calamine'] | None = ..., converters: Mapping[int | str, Callable[[object], object]] | None = ..., true_values: Iterable[Hashable] | None = ..., false_values: Iterable[Hashable] | None = ..., skiprows: int | Sequence[int] | Callable[[object], bool] | None = ..., nrows: int | None = ..., na_values: Sequence[str] | dict[str | int, Sequence[str]] = ..., keep_default_na: bool = ..., na_filter: bool = ..., verbose: bool = ..., parse_dates: bool | Sequence[int] | Sequence[Sequence[str] | Sequence[int]] | dict[str, Sequence[int] | list[str]] = ..., date_format: dict[Hashable, str] | str | None = ..., thousands: str | None = ..., decimal: str = ..., comment: str | None = ..., skipfooter: int = ..., storage_options: dict[str, Any] | None = ..., dtype_backend: Literal['pyarrow', 'numpy_nullable'] | Literal[_NoDefault.no_default] = ...) -> dict[str, DataFrame]
+ python/pyspark/pandas/namespace.py:1139: note: def read_excel(io: str | PathLike[str] | ReadBuffer[bytes] | ExcelFile | Any | Any | Any | Any, sheet_name: None, *, header: int | Sequence[int] | None = ..., names: MutableSequence[Any] | ndarray[tuple[int, ...], dtype[Any]] | tuple[Any, ...] | range | None = ..., index_col: int | Sequence[int] | str | None = ..., usecols: str | SequenceNotStr[Hashable] | range | ExtensionArray | ndarray[Any, Any] | Index[Any] | Series[Any] | Callable[[Any], bool] | None = ..., dtype: str | ExtensionDtype | str | dtype[generic[Any]] | type[str] | type[complex] | type[bool] | type[object] | Mapping[str, str | ExtensionDtype | str | dtype[generic[Any]] | type[str] | type[complex] | type[bool] | type[object]] | None = ..., engine: Literal['xlrd', 'openpyxl', 'odf', 'pyxlsb', 'calamine'] | None = ..., converters: Mapping[int | str, Callable[[object], object]] | None = ..., true_values: Iterable[Hashable] | None = ..., false_values: Iterable[Hashable] | None = ..., skiprows: int | Sequence[int] | Callable[[object], bool] | None = ..., nrows: int | None = ..., na_values: Sequence[str] | dict[str | int, Sequence[str]] = ..., keep_default_na: bool = ..., na_filter: bool = ..., verbose: bool = ..., parse_dates: bool | Sequence[int] | Sequence[Sequence[str] | Sequence[int]] | dict[str, Sequence[int] | list[str]] = ..., date_format: dict[Hashable, str] | str | None = ..., thousands: str | None = ..., decimal: str = ..., comment: str | None = ..., skipfooter: int = ..., storage_options: dict[str, Any] | None = ..., dtype_backend: Literal['pyarrow', 'numpy_nullable'] | Literal[_NoDefault.no_default] = ...) -> dict[str, DataFrame]
- python/pyspark/pandas/namespace.py:1139: note: def read_excel(io: str | PathLike[str] | ReadBuffer[bytes] | ExcelFile | Any | Any | Any | Any, sheet_name: list[int | str], *, header: int | Sequence[int] | None = ..., names: MutableSequence[Any] | ndarray[Any, Any] | tuple[Any, ...] | range | None = ..., index_col: int | Sequence[int] | str | None = ..., usecols: str | SequenceNotStr[Hashable] | range | ExtensionArray | ndarray[Any, Any] | Index[Any] | Series[Any] | Callable[[Any], bool] | None = ..., dtype: str | ExtensionDtype | str | dtype[generic[Any]] | type[str] | type[complex] | type[bool] | type[object] | Mapping[str, str | ExtensionDtype | str | dtype[generic[Any]] | type[str] | type[complex] | type[bool] | type[object]] | None = ..., engine: Literal['xlrd', 'openpyxl', 'odf', 'pyxlsb', 'calamine'] | None = ..., converters: Mapping[int | str, Callable[[object], object]] | None = ..., true_values: Iterable[Hashable] | None = ..., false_values: Iterable[Hashable] | None = ..., skiprows: int | Sequence[int] | Callable[[object], bool] | None = ..., nrows: int | None = ..., na_values: Sequence[str] | dict[str | int, Sequence[str]] = ..., keep_default_na: bool = ..., na_filter: bool = ..., verbose: bool = ..., parse_dates: bool | Sequence[int] | Sequence[Sequence[str] | Sequence[int]] | dict[str, Sequence[int] | list[str]] = ..., date_format: dict[Hashable, str] | str | None = ..., thousands: str | None = ..., decimal: str = ..., comment: str | None = ..., skipfooter: int = ..., storage_options: dict[str, Any] | None = ..., dtype_backend: Literal['pyarrow', 'numpy_nullable'] | Literal[_NoDefault.no_default] = ...) -> dict[int | str, DataFrame]
+ python/pyspark/pandas/namespace.py:1139: note: def read_excel(io: str | PathLike[str] | ReadBuffer[bytes] | ExcelFile | Any | Any | Any | Any, sheet_name: list[int | str], *, header: int | Sequence[int] | None = ..., names: MutableSequence[Any] | ndarray[tuple[int, ...], dtype[Any]] | tuple[Any, ...] | range | None = ..., index_col: int | Sequence[int] | str | None = ..., usecols: str | SequenceNotStr[Hashable] | range | ExtensionArray | ndarray[Any, Any] | Index[Any] | Series[Any] | Callable[[Any], bool] | None = ..., dtype: str | ExtensionDtype | str | dtype[generic[Any]] | type[str] | type[complex] | type[bool] | type[object] | Mapping[str, str | ExtensionDtype | str | dtype[generic[Any]] | type[str] | type[complex] | type[bool] | type[object]] | None = ..., engine: Literal['xlrd', 'openpyxl', 'odf', 'pyxlsb', 'calamine'] | None = ..., converters: Mapping[int | str, Callable[[object], object]] | None = ..., true_values: Iterable[Hashable] | None = ..., false_values: Iterable[Hashable] | None = ..., skiprows: int | Sequence[int] | Callable[[object], bool] | None = ..., nrows: int | None = ..., na_values: Sequence[str] | dict[str | int, Sequence[str]] = ..., keep_default_na: bool = ..., na_filter: bool = ..., verbose: bool = ..., parse_dates: bool | Sequence[int] | Sequence[Sequence[str] | Sequence[int]] | dict[str, Sequence[int] | list[str]] = ..., date_format: dict[Hashable, str] | str | None = ..., thousands: str | None = ..., decimal: str = ..., comment: str | None = ..., skipfooter: int = ..., storage_options: dict[str, Any] | None = ..., dtype_backend: Literal['pyarrow', 'numpy_nullable'] | Literal[_NoDefault.no_default] = ...) -> dict[int | str, DataFrame]
- python/pyspark/pandas/namespace.py:1139: note: def read_excel(io: str | PathLike[str] | ReadBuffer[bytes] | ExcelFile | Any | Any | Any | Any, sheet_name: int | str = ..., *, header: int | Sequence[int] | None = ..., names: MutableSequence[Any] | ndarray[Any, Any] | tuple[Any, ...] | range | None = ..., index_col: int | Sequence[int] | str | None = ..., usecols: str | SequenceNotStr[Hashable] | range | ExtensionArray | ndarray[Any, Any] | Index[Any] | Series[Any] | Callable[[Any], bool] | None = ..., dtype: str | ExtensionDtype | str | dtype[generic[Any]] | type[str] | type[complex] | type[bool] | type[object] | Mapping[str, str | ExtensionDtype | str | dtype[generic[Any]] | type[str] | type[complex] | type[bool] | type[object]] | None = ..., engine: Literal['xlrd', 'openpyxl', 'odf', 'pyxlsb', 'calamine'] | None = ..., converters: Mapping[int | str, Callable[[object], object]] | None = ..., true_values: Iterable[Hashable] | None = ..., false_values: Iterable[Hashable] | None = ..., skiprows: int | Sequence[int] | Callable[[object], bool] | None = ..., nrows: int | None = ..., na_values: Sequence[str] | dict[str | int, Sequence[str]] = ..., keep_default_na: bool = ..., na_filter: bool = ..., verbose: bool = ..., parse_dates: bool | Sequence[int] | Sequence[Sequence[str] | Sequence[int]] | dict[str, Sequence[int] | list[str]] = ..., date_format: dict[Hashable, str] | str | None = ..., thousands: str | None = ..., decimal: str = ..., comment: str | None = ..., skipfooter: int = ..., storage_options: dict[str, Any] | None = ..., dtype_backend: Literal['pyarrow', 'numpy_nullable'] | Literal[_NoDefault.no_default] = ...) -> DataFrame
+ python/pyspark/pandas/namespace.py:1139: note: def read_excel(io: str | PathLike[str] | ReadBuffer[bytes] | ExcelFile | Any | Any | Any | Any, sheet_name: int | str = ..., *, header: int | Sequence[int] | None = ..., names: MutableSequence[Any] | ndarray[tuple[int, ...], dtype[Any]] | tuple[Any, ...] | range | None = ..., index_col: int | Sequence[int] | str | None = ..., usecols: str | SequenceNotStr[Hashable] | range | ExtensionArray | ndarray[Any, Any] | Index[Any] | Series[Any] | Callable[[Any], bool] | None = ..., dtype: str | ExtensionDtype | str | dtype[generic[Any]] | type[str] | type[complex] | type[bool] | type[object] | Mapping[str, str | ExtensionDtype | str | dtype[generic[Any]] | type[str] | type[complex] | type[bool] | type[object]] | None = ..., engine: Literal['xlrd', 'openpyxl', 'odf', 'pyxlsb', 'calamine'] | None = ..., converters: Mapping[int | str, Callable[[object], object]] | None = ..., true_values: Iterable[Hashable] | None = ..., false_values: Iterable[Hashable] | None = ..., skiprows: int | Sequence[int] | Callable[[object], bool] | None = ..., nrows: int | None = ..., na_values: Sequence[str] | dict[str | int, Sequence[str]] = ..., keep_default_na: bool = ..., na_filter: bool = ..., verbose: bool = ..., parse_dates: bool | Sequence[int] | Sequence[Sequence[str] | Sequence[int]] | dict[str, Sequence[int] | list[str]] = ..., date_format: dict[Hashable, str] | str | None = ..., thousands: str | None = ..., decimal: str = ..., comment: str | None = ..., skipfooter: int = ..., storage_options: dict[str, Any] | None = ..., dtype_backend: Literal['pyarrow', 'numpy_nullable'] | Literal[_NoDefault.no_default] = ...) -> DataFrame
+ python/pyspark/ml/linalg/__init__.py:1145: error: Incompatible types in assignment (expression has type "ndarray[tuple[int, ...], dtype[Any]]", variable has type "ndarray[tuple[int], dtype[Any]]") [assignment]
+ python/pyspark/mllib/linalg/__init__.py:1327: error: Incompatible types in assignment (expression has type "ndarray[tuple[int, ...], dtype[Any]]", variable has type "ndarray[tuple[int], dtype[Any]]") [assignment]
scikit-learn (https://github.com/scikit-learn/scikit-learn)
+ sklearn/model_selection/tests/test_search.py:2894: error: Unused "type: ignore" comment [unused-ignore]
pandera (https://github.com/pandera-dev/pandera)
- pandera/strategies/pandas_strategies.py:71: note: def mask(self, cond: Series[Any] | Series[bool] | ndarray[Any, Any] | Callable[[Series[Any]], Series[bool]] | Callable[[Any], bool], other: str | bytes | date | datetime | timedelta | <12 more items> | None = ..., *, inplace: Literal[True], axis: Literal['index', 0] | None = ..., level: Hashable | int | None = ...) -> None
+ pandera/strategies/pandas_strategies.py:71: note: def mask(self, cond: Series[Any] | Series[bool] | ndarray[tuple[int, ...], dtype[Any]] | Callable[[Series[Any]], Series[bool]] | Callable[[Any], bool], other: str | bytes | date | datetime | timedelta | <12 more items> | None = ..., *, inplace: Literal[True], axis: Literal['index', 0] | None = ..., level: Hashable | int | None = ...) -> None
- pandera/strategies/pandas_strategies.py:71: note: def mask(self, cond: Series[Any] | Series[bool] | ndarray[Any, Any] | Callable[[Series[Any]], Series[bool]] | Callable[[Any], bool], other: str | bytes | date | datetime | timedelta | <12 more items> | None = ..., *, inplace: Literal[False] = ..., axis: Literal['index', 0] | None = ..., level: Hashable | int | None = ...) -> Series[Any]
+ pandera/strategies/pandas_strategies.py:71: note: def mask(self, cond: Series[Any] | Series[bool] | ndarray[tuple[int, ...], dtype[Any]] | Callable[[Series[Any]], Series[bool]] | Callable[[Any], bool], other: str | bytes | date | datetime | timedelta | <12 more items> | None = ..., *, inplace: Literal[False] = ..., axis: Literal['index', 0] | None = ..., level: Hashable | int | None = ...) -> Series[Any]
- pandera/strategies/pandas_strategies.py:73: note: def mask(self, cond: Series[Any] | Series[bool] | ndarray[Any, Any] | Callable[[Series[Any]], Series[bool]] | Callable[[Any], bool], other: str | bytes | date | datetime | timedelta | <12 more items> | None = ..., *, inplace: Literal[True], axis: Literal['index', 0] | None = ..., level: Hashable | int | None = ...) -> None
+ pandera/strategies/pandas_strategies.py:73: note: def mask(self, cond: Series[Any] | Series[bool] | ndarray[tuple[int, ...], dtype[Any]] | Callable[[Series[Any]], Series[bool]] | Callable[[Any], bool], other: str | bytes | date | datetime | timedelta | <12 more items> | None = ..., *, inplace: Literal[True], axis: Literal['index', 0] | None = ..., level: Hashable | int | None = ...) -> None
- pandera/strategies/pandas_strategies.py:73: note: def mask(self, cond: Series[Any] | Series[bool] | ndarray[Any, Any] | Callable[[Series[Any]], Series[bool]] | Callable[[Any], bool], other: str | bytes | date | datetime | timedelta | <12 more items> | None = ..., *, inplace: Literal[False] = ..., axis: Literal['index', 0] | None = ..., level: Hashable | int | None = ...) -> Series[Any]
+ pandera/strategies/pandas_strategies.py:73: note: def mask(self, cond: Series[Any] | Series[bool] | ndarray[tuple[int, ...], dtype[Any]] | Callable[[Series[Any]], Series[bool]] | Callable[[Any], bool], other: str | bytes | date | datetime | timedelta | <12 more items> | None = ..., *, inplace: Literal[False] = ..., axis: Literal['index', 0] | None = ..., level: Hashable | int | None = ...) -> Series[Any]
- pandera/strategies/pandas_strategies.py:74: note: def mask(self, cond: Series[Any] | Series[bool] | ndarray[Any, Any] | Callable[[Series[Any]], Series[bool]] | Callable[[Any], bool], other: str | bytes | date | datetime | timedelta | <12 more items> | None = ..., *, inplace: Literal[True], axis: Literal['index', 0] | None = ..., level: Hashable | int | None = ...) -> None
+ pandera/strategies/pandas_strategies.py:74: note: def mask(self, cond: Series[Any] | Series[bool] | ndarray[tuple[int, ...], dtype[Any]] | Callable[[Series[Any]], Series[bool]] | Callable[[Any], bool], other: str | bytes | date | datetime | timedelta | <12 more items> | None = ..., *, inplace: Literal[True], axis: Literal['index', 0] | None = ..., level: Hashable | int | None = ...) -> None
- pandera/strategies/pandas_strategies.py:74: note: def mask(self, cond: Series[Any] | Series[bool] | ndarray[Any, Any] | Callable[[Series[Any]], Series[bool]] | Callable[[Any], bool], other: str | bytes | date | datetime | timedelta | <12 more items> | None = ..., *, inplace: Literal[False] = ..., axis: Literal['index', 0] | None = ..., level: Hashable | int | None = ...) -> Series[Any]
+ pandera/strategies/pandas_strategies.py:74: note: def mask(self, cond: Series[Any] | Series[bool] | ndarray[tuple[int, ...], dtype[Any]] | Callable[[Series[Any]], Series[bool]] | Callable[[Any], bool], other: str | bytes | date | datetime | timedelta | <12 more items> | None = ..., *, inplace: Literal[False] = ..., axis: Literal['index', 0] | None = ..., level: Hashable | int | None = ...) -> Series[Any]
arviz (https://github.com/arviz-devs/arviz)
- arviz/stats/ecdf_utils.py:83: error: Incompatible return value type (got "tuple[float | ndarray[tuple[int, ...], dtype[float64]], float | ndarray[tuple[int, ...], dtype[float64]]]", expected "tuple[ndarray[Any, Any], ndarray[Any, Any]]") [return-value]
+ arviz/stats/ecdf_utils.py:83: error: Incompatible return value type (got "tuple[float | ndarray[tuple[int, ...], dtype[float64]], float | ndarray[tuple[int, ...], dtype[float64]]]", expected "tuple[ndarray[tuple[int, ...], dtype[Any]], ndarray[tuple[int, ...], dtype[Any]]]") [return-value]
|
Looks like the fix for |
|
This is ready to merge, as far as I'm concerned. |
OK then, in it goes. Thanks Joren,. |
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Static type-checkers now consider
_: np.ndarrayas equivalent to_: npt.NDArray[Any].Similarly,
_: np.flatiterwill now be considered equivalent to_: np.flatiter[np.ndarray].