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Removing dict calls #38276

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Merged
merged 3 commits into from
Dec 4, 2020
Merged

Removing dict calls #38276

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b755d98
CLN: removed dict calls for /core/arrays/floating.py
joao-zanutto Dec 3, 2020
4c6072e
CLN: removed dict calls for /core/arrays/interval.py and /core/arrays…
joao-zanutto Dec 3, 2020
56a6d20
Merge branch 'master' into removing-dict-calls
joao-zanutto Dec 3, 2020
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4 changes: 2 additions & 2 deletions pandas/core/arrays/floating.py
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Original file line number Diff line number Diff line change
Expand Up @@ -386,9 +386,9 @@ def astype(self, dtype, copy: bool = True) -> ArrayLike:
# coerce
if is_float_dtype(dtype):
# In astype, we consider dtype=float to also mean na_value=np.nan
kwargs = dict(na_value=np.nan)
kwargs = {"na_value": np.nan}
elif is_datetime64_dtype(dtype):
kwargs = dict(na_value=np.datetime64("NaT"))
kwargs = {"na_value": np.datetime64("NaT")}
else:
kwargs = {}

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18 changes: 9 additions & 9 deletions pandas/core/arrays/numpy_.py
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Expand Up @@ -273,12 +273,12 @@ def _values_for_factorize(self) -> Tuple[np.ndarray, int]:
# Reductions

def any(self, *, axis=None, out=None, keepdims=False, skipna=True):
nv.validate_any((), dict(out=out, keepdims=keepdims))
nv.validate_any((), {"out": out, "keepdims": keepdims})
result = nanops.nanany(self._ndarray, axis=axis, skipna=skipna)
return self._wrap_reduction_result(axis, result)

def all(self, *, axis=None, out=None, keepdims=False, skipna=True):
nv.validate_all((), dict(out=out, keepdims=keepdims))
nv.validate_all((), {"out": out, "keepdims": keepdims})
result = nanops.nanall(self._ndarray, axis=axis, skipna=skipna)
return self._wrap_reduction_result(axis, result)

Expand Down Expand Up @@ -311,15 +311,15 @@ def prod(self, *, axis=None, skipna=True, min_count=0, **kwargs) -> Scalar:
return self._wrap_reduction_result(axis, result)

def mean(self, *, axis=None, dtype=None, out=None, keepdims=False, skipna=True):
nv.validate_mean((), dict(dtype=dtype, out=out, keepdims=keepdims))
nv.validate_mean((), {"dtype": dtype, "out": out, "keepdims": keepdims})
result = nanops.nanmean(self._ndarray, axis=axis, skipna=skipna)
return self._wrap_reduction_result(axis, result)

def median(
self, *, axis=None, out=None, overwrite_input=False, keepdims=False, skipna=True
):
nv.validate_median(
(), dict(out=out, overwrite_input=overwrite_input, keepdims=keepdims)
(), {"out": out, "overwrite_input": overwrite_input, "keepdims": keepdims}
)
result = nanops.nanmedian(self._ndarray, axis=axis, skipna=skipna)
return self._wrap_reduction_result(axis, result)
Expand All @@ -328,7 +328,7 @@ def std(
self, *, axis=None, dtype=None, out=None, ddof=1, keepdims=False, skipna=True
):
nv.validate_stat_ddof_func(
(), dict(dtype=dtype, out=out, keepdims=keepdims), fname="std"
(), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="std"
)
result = nanops.nanstd(self._ndarray, axis=axis, skipna=skipna, ddof=ddof)
return self._wrap_reduction_result(axis, result)
Expand All @@ -337,7 +337,7 @@ def var(
self, *, axis=None, dtype=None, out=None, ddof=1, keepdims=False, skipna=True
):
nv.validate_stat_ddof_func(
(), dict(dtype=dtype, out=out, keepdims=keepdims), fname="var"
(), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="var"
)
result = nanops.nanvar(self._ndarray, axis=axis, skipna=skipna, ddof=ddof)
return self._wrap_reduction_result(axis, result)
Expand All @@ -346,21 +346,21 @@ def sem(
self, *, axis=None, dtype=None, out=None, ddof=1, keepdims=False, skipna=True
):
nv.validate_stat_ddof_func(
(), dict(dtype=dtype, out=out, keepdims=keepdims), fname="sem"
(), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="sem"
)
result = nanops.nansem(self._ndarray, axis=axis, skipna=skipna, ddof=ddof)
return self._wrap_reduction_result(axis, result)

def kurt(self, *, axis=None, dtype=None, out=None, keepdims=False, skipna=True):
nv.validate_stat_ddof_func(
(), dict(dtype=dtype, out=out, keepdims=keepdims), fname="kurt"
(), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="kurt"
)
result = nanops.nankurt(self._ndarray, axis=axis, skipna=skipna)
return self._wrap_reduction_result(axis, result)

def skew(self, *, axis=None, dtype=None, out=None, keepdims=False, skipna=True):
nv.validate_stat_ddof_func(
(), dict(dtype=dtype, out=out, keepdims=keepdims), fname="skew"
(), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="skew"
)
result = nanops.nanskew(self._ndarray, axis=axis, skipna=skipna)
return self._wrap_reduction_result(axis, result)
Expand Down