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Merged
merged 4 commits into from
May 3, 2022
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STYL: a few cleanups in pyi files #46921

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ec4b739
STYL: a few cleanups in pyi files
twoertwein May 1, 2022
1961c03
no default arguments in pyi
twoertwein May 2, 2022
13a6299
TYP: enable reportInvalidStubStatement
twoertwein May 2, 2022
ff5bcef
remove _unique
twoertwein May 2, 2022
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10 changes: 5 additions & 5 deletions pandas/_libs/hashtable.pyi
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Expand Up @@ -163,7 +163,7 @@ class HashTable:
na_sentinel: int = ...,
na_value: object = ...,
mask=...,
) -> tuple[np.ndarray, npt.NDArray[np.intp],]: ... # np.ndarray[subclass-specific]
) -> tuple[np.ndarray, npt.NDArray[np.intp]]: ... # np.ndarray[subclass-specific]
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_unique above only returns tuple if return_inverse is True, which is how it's called from factorize. The return type of _unique is a Union, but these are more easily avoided with overloads in stubs.

I assume that _unique is intended to be private so we probably shouldn't be calling it from outside, so rather than adding overloads could just remove _unique from the stub.

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I removed _unique but haven't yet added overloads to unique as I embarrassingly couldn't find the implementation.

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it's created from a template. pandas/_libs/hashtable_class_helper.pxi.in

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Thanks! Would need to first deprecate return_inverse as a positional argument. Can I just use the normal deprecation decorator in cython (or would that have an unintended performance hit).

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I doubt HashTable is part of the public API of pandas but is obviously not private to the module as it is used elsewhere. Can probably just make the arguments keyword only without deprecation. (OK as follow-up, for our internal consistency checking, if we do not have any errors reported then not a priority to add overloads if not public)


class Complex128HashTable(HashTable): ...
class Complex64HashTable(HashTable): ...
Expand All @@ -175,7 +175,7 @@ class Int64HashTable(HashTable):
def get_labels_groupby(
self,
values: npt.NDArray[np.int64], # const int64_t[:]
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.int64],]: ...
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.int64]]: ...
def map_keys_to_values(
self,
keys: npt.NDArray[np.int64],
Expand All @@ -198,13 +198,13 @@ def duplicated(
keep: Literal["last", "first", False] = ...,
) -> npt.NDArray[np.bool_]: ...
def mode(
values: np.ndarray, dropna: bool, mask: npt.NDArray[np.bool_] | None = None
values: np.ndarray, dropna: bool, mask: npt.NDArray[np.bool_] | None = ...
) -> np.ndarray: ...
def value_count(
values: np.ndarray,
dropna: bool,
mask: npt.NDArray[np.bool_] | None = None,
) -> tuple[np.ndarray, npt.NDArray[np.int64],]: ... # np.ndarray[same-as-values]
mask: npt.NDArray[np.bool_] | None = ...,
) -> tuple[np.ndarray, npt.NDArray[np.int64]]: ... # np.ndarray[same-as-values]

# arr and values should have same dtype
def ismember(
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4 changes: 2 additions & 2 deletions pandas/_libs/tslibs/conversion.pyi
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Expand Up @@ -14,7 +14,7 @@ class OutOfBoundsTimedelta(ValueError): ...

def precision_from_unit(
unit: str,
) -> tuple[int, int,]: ... # (int64_t, _)
) -> tuple[int, int]: ... # (int64_t, _)
def ensure_datetime64ns(
arr: np.ndarray, # np.ndarray[datetime64[ANY]]
copy: bool = ...,
Expand All @@ -25,5 +25,5 @@ def ensure_timedelta64ns(
) -> np.ndarray: ... # np.ndarray[timedelta64ns]
def datetime_to_datetime64(
values: npt.NDArray[np.object_],
) -> tuple[np.ndarray, tzinfo | None,]: ... # (np.ndarray[dt64ns], _)
) -> tuple[np.ndarray, tzinfo | None]: ... # (np.ndarray[dt64ns], _)
def localize_pydatetime(dt: datetime, tz: tzinfo | None) -> datetime: ...
2 changes: 0 additions & 2 deletions pandas/_libs/tslibs/dtypes.pyi
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@@ -1,7 +1,5 @@
from enum import Enum

from pandas._libs.tslibs.offsets import BaseOffset

# These are not public API, but are exposed in the .pyi file because they
# are imported in tests.
_attrname_to_abbrevs: dict[str, str]
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1 change: 0 additions & 1 deletion pandas/_libs/tslibs/timestamps.pyi
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Expand Up @@ -16,7 +16,6 @@ import numpy as np

from pandas._libs.tslibs import (
BaseOffset,
NaTType,
Period,
Tick,
Timedelta,
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1 change: 0 additions & 1 deletion pandas/conftest.py
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Expand Up @@ -17,7 +17,6 @@
- Dtypes
- Misc
"""
# pyright: reportUntypedFunctionDecorator = false

from collections import abc
from datetime import (
Expand Down
29 changes: 8 additions & 21 deletions pyproject.toml
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Expand Up @@ -153,34 +153,21 @@ pythonVersion = "3.8"
typeCheckingMode = "basic"
include = ["pandas", "typings"]
exclude = ["pandas/tests", "pandas/io/clipboard", "pandas/util/version"]
# enable subset of "strict"
reportDuplicateImport = true
reportInvalidStubStatement = true
reportPropertyTypeMismatch = true
reportUntypedClassDecorator = true
reportUntypedFunctionDecorator = true
reportUntypedNamedTuple = true
# disable subset of "basic"
reportGeneralTypeIssues = false
reportConstantRedefinition = false
reportFunctionMemberAccess = false
reportImportCycles = false
reportIncompatibleMethodOverride = false
reportIncompatibleVariableOverride = false
reportMissingModuleSource = false
reportMissingParameterType = false
reportMissingTypeArgument = false
reportMissingTypeStubs = false
reportOptionalCall = false
reportOptionalIterable = false
reportOptionalMemberAccess = false
reportOptionalOperand = false
reportOptionalSubscript = false
reportPrivateImportUsage = false
reportPrivateUsage = false
reportUnboundVariable = false
reportUnknownArgumentType = false
reportUnknownLambdaType = false
reportUnknownMemberType = false
reportUnknownParameterType = false
reportUnknownVariableType = false
reportUnnecessaryComparison = false
reportUnnecessaryIsInstance = false
reportUnsupportedDunderAll = false
reportUntypedBaseClass = false
reportUnusedClass = false
reportUnusedFunction = false
reportUnusedImport = false
reportUnusedVariable = false