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STYLE enable pylint's redefined-outer-name #49671

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Merged
merged 4 commits into from
Nov 14, 2022
Merged

STYLE enable pylint's redefined-outer-name #49671

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eac9f7e
fix warning for pandas/core/dtypes/cast.py, pandas/core/dtypes/dtypes…
Nov 12, 2022
e0a9d0b
fix warning for pandas/core/dtypes/cast.py, pandas/core/dtypes/dtypes…
Nov 12, 2022
fa7390d
fix warning for pandas/core/dtypes/cast.py, pandas/core/dtypes/dtypes…
Nov 12, 2022
2ba7b20
fix warning for pandas/core/dtypes/cast.py, pandas/core/dtypes/dtypes…
Nov 12, 2022
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30 changes: 15 additions & 15 deletions pandas/core/dtypes/cast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,8 +5,8 @@
from __future__ import annotations

from datetime import (
datetime,
timedelta,
datetime as datetime_func,
timedelta as timedelta_func,
)
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would import datetime as dt (and then dt.datetime, dt.timedelta) work?

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sounds good.

import functools
from typing import (
Expand Down Expand Up @@ -73,7 +73,7 @@
is_string_dtype,
is_timedelta64_dtype,
is_unsigned_integer_dtype,
pandas_dtype,
pandas_dtype as pandas_dtype_func,
)
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
Expand Down Expand Up @@ -170,9 +170,9 @@ def maybe_box_datetimelike(value: Scalar, dtype: Dtype | None = None) -> Scalar:
"""
if dtype == _dtype_obj:
pass
elif isinstance(value, (np.datetime64, datetime)):
elif isinstance(value, (np.datetime64, datetime_func)):
value = Timestamp(value)
elif isinstance(value, (np.timedelta64, timedelta)):
elif isinstance(value, (np.timedelta64, timedelta_func)):
value = Timedelta(value)

return value
Expand Down Expand Up @@ -761,7 +761,7 @@ def infer_dtype_from_scalar(val, pandas_dtype: bool = False) -> tuple[DtypeObj,

dtype = _dtype_obj

elif isinstance(val, (np.datetime64, datetime)):
elif isinstance(val, (np.datetime64, datetime_func)):
try:
val = Timestamp(val)
except OutOfBoundsDatetime:
Expand All @@ -781,7 +781,7 @@ def infer_dtype_from_scalar(val, pandas_dtype: bool = False) -> tuple[DtypeObj,
# return datetimetz as object
return _dtype_obj, val

elif isinstance(val, (np.timedelta64, timedelta)):
elif isinstance(val, (np.timedelta64, timedelta_func)):
try:
val = Timedelta(val)
except (OutOfBoundsTimedelta, OverflowError):
Expand Down Expand Up @@ -1096,10 +1096,10 @@ def convert_dtypes(
if not convert_string or inferred_dtype == "bytes":
return input_array.dtype
else:
return pandas_dtype("string")
return pandas_dtype_func("string")

if convert_integer:
target_int_dtype = pandas_dtype("Int64")
target_int_dtype = pandas_dtype_func("Int64")

if is_integer_dtype(input_array.dtype):
from pandas.core.arrays.integer import INT_STR_TO_DTYPE
Expand Down Expand Up @@ -1128,15 +1128,15 @@ def convert_dtypes(
from pandas.core.arrays.floating import FLOAT_STR_TO_DTYPE

inferred_float_dtype: DtypeObj = FLOAT_STR_TO_DTYPE.get(
input_array.dtype.name, pandas_dtype("Float64")
input_array.dtype.name, pandas_dtype_func("Float64")
)
# if we could also convert to integer, check if all floats
# are actually integers
if convert_integer:
# TODO: de-dup with maybe_cast_to_integer_array?
arr = input_array[notna(input_array)]
if (arr.astype(int) == arr).all():
inferred_dtype = pandas_dtype("Int64")
inferred_dtype = pandas_dtype_func("Int64")
else:
inferred_dtype = inferred_float_dtype
else:
Expand All @@ -1146,13 +1146,13 @@ def convert_dtypes(
and is_object_dtype(input_array.dtype)
and inferred_dtype == "mixed-integer-float"
):
inferred_dtype = pandas_dtype("Float64")
inferred_dtype = pandas_dtype_func("Float64")

if convert_boolean:
if is_bool_dtype(input_array.dtype):
inferred_dtype = pandas_dtype("boolean")
inferred_dtype = pandas_dtype_func("boolean")
elif isinstance(inferred_dtype, str) and inferred_dtype == "boolean":
inferred_dtype = pandas_dtype("boolean")
inferred_dtype = pandas_dtype_func("boolean")

if isinstance(inferred_dtype, str):
# If we couldn't do anything else, then we retain the dtype
Expand Down Expand Up @@ -1578,7 +1578,7 @@ def construct_1d_arraylike_from_scalar(
def _maybe_box_and_unbox_datetimelike(value: Scalar, dtype: DtypeObj):
# Caller is responsible for checking dtype.kind in ["m", "M"]

if isinstance(value, datetime):
if isinstance(value, datetime_func):
# we dont want to box dt64, in particular datetime64("NaT")
value = maybe_box_datetimelike(value, dtype)

Expand Down
17 changes: 9 additions & 8 deletions pandas/core/dtypes/dtypes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,13 +17,14 @@
from pandas._libs import missing as libmissing
from pandas._libs.interval import Interval
from pandas._libs.properties import cache_readonly
from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
from pandas._libs.tslibs.dtypes import PeriodDtypeBase
from pandas._libs.tslibs import (
BaseOffset,
NaT,
NaTType,
Period,
Timestamp,
dtypes,
timezones,
to_offset,
tz_compare,
Expand Down Expand Up @@ -716,10 +717,10 @@ def _creso(self) -> int:
The NPY_DATETIMEUNIT corresponding to this dtype's resolution.
"""
reso = {
"s": dtypes.NpyDatetimeUnit.NPY_FR_s,
"ms": dtypes.NpyDatetimeUnit.NPY_FR_ms,
"us": dtypes.NpyDatetimeUnit.NPY_FR_us,
"ns": dtypes.NpyDatetimeUnit.NPY_FR_ns,
"s": NpyDatetimeUnit.NPY_FR_s,
"ms": NpyDatetimeUnit.NPY_FR_ms,
"us": NpyDatetimeUnit.NPY_FR_us,
"ns": NpyDatetimeUnit.NPY_FR_ns,
}[self.unit]
return reso.value

Expand Down Expand Up @@ -820,7 +821,7 @@ def __setstate__(self, state) -> None:


@register_extension_dtype
class PeriodDtype(dtypes.PeriodDtypeBase, PandasExtensionDtype):
class PeriodDtype(PeriodDtypeBase, PandasExtensionDtype):
"""
An ExtensionDtype for Period data.

Expand Down Expand Up @@ -869,7 +870,7 @@ def __new__(cls, freq=None):
elif freq is None:
# empty constructor for pickle compat
# -10_000 corresponds to PeriodDtypeCode.UNDEFINED
u = dtypes.PeriodDtypeBase.__new__(cls, -10_000)
u = PeriodDtypeBase.__new__(cls, -10_000)
u._freq = None
return u

Expand All @@ -880,7 +881,7 @@ def __new__(cls, freq=None):
return cls._cache_dtypes[freq.freqstr]
except KeyError:
dtype_code = freq._period_dtype_code
u = dtypes.PeriodDtypeBase.__new__(cls, dtype_code)
u = PeriodDtypeBase.__new__(cls, dtype_code)
u._freq = freq
cls._cache_dtypes[freq.freqstr] = u
return u
Expand Down
5 changes: 2 additions & 3 deletions pandas/core/indexes/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -134,10 +134,9 @@
is_valid_na_for_dtype,
isna,
)

from pandas.core import (
missing as missing_func,
arraylike,
missing,
ops,
)
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missing isn't a function, so missing_func wouldn't be the best name

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what was the issue with what you had before (from pandas.core.missing import clean_reindex_fill_method)?

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The build pipeline failed at https://github.com/pandas-dev/pandas/actions/runs/3452096357/jobs/5761771671 which is failing still with missing_func. from pandas.core.missing import clean_reindex_fill_method worked locally but since pipeline failed , chaged it.

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it was fine, you just need to run pre-commit on the files you've changed, check the contributing guide for how to do that

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Thanks did run it.

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cool, should be good now thanks

from pandas.core.accessor import CachedAccessor
Expand Down Expand Up @@ -3650,7 +3649,7 @@ def get_indexer(
limit: int | None = None,
tolerance=None,
) -> npt.NDArray[np.intp]:
method = missing.clean_reindex_fill_method(method)
method = missing_func.clean_reindex_fill_method(method)
orig_target = target
target = self._maybe_cast_listlike_indexer(target)

Expand Down