in amplify/backend/function/iamxawswrangler/lib/python/pandas/core/generic.py [0:0]
def _add_numeric_operations(cls):
"""
Add the operations to the cls; evaluate the doc strings again
"""
axis_descr, name1, name2 = _doc_params(cls)
@doc(
_bool_doc,
desc=_any_desc,
name1=name1,
name2=name2,
axis_descr=axis_descr,
see_also=_any_see_also,
examples=_any_examples,
empty_value=False,
)
def any(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs):
return NDFrame.any(self, axis, bool_only, skipna, level, **kwargs)
setattr(cls, "any", any)
@doc(
_bool_doc,
desc=_all_desc,
name1=name1,
name2=name2,
axis_descr=axis_descr,
see_also=_all_see_also,
examples=_all_examples,
empty_value=True,
)
def all(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs):
return NDFrame.all(self, axis, bool_only, skipna, level, **kwargs)
setattr(cls, "all", all)
# error: Argument 1 to "doc" has incompatible type "Optional[str]"; expected
# "Union[str, Callable[..., Any]]"
@doc(
NDFrame.mad.__doc__, # type: ignore[arg-type]
desc="Return the mean absolute deviation of the values "
"over the requested axis.",
name1=name1,
name2=name2,
axis_descr=axis_descr,
see_also="",
examples="",
)
def mad(self, axis=None, skipna=None, level=None):
return NDFrame.mad(self, axis, skipna, level)
setattr(cls, "mad", mad)
@doc(
_num_ddof_doc,
desc="Return unbiased standard error of the mean over requested "
"axis.\n\nNormalized by N-1 by default. This can be changed "
"using the ddof argument",
name1=name1,
name2=name2,
axis_descr=axis_descr,
)
def sem(
self,
axis=None,
skipna=None,
level=None,
ddof=1,
numeric_only=None,
**kwargs,
):
return NDFrame.sem(self, axis, skipna, level, ddof, numeric_only, **kwargs)
setattr(cls, "sem", sem)
@doc(
_num_ddof_doc,
desc="Return unbiased variance over requested axis.\n\nNormalized by "
"N-1 by default. This can be changed using the ddof argument",
name1=name1,
name2=name2,
axis_descr=axis_descr,
)
def var(
self,
axis=None,
skipna=None,
level=None,
ddof=1,
numeric_only=None,
**kwargs,
):
return NDFrame.var(self, axis, skipna, level, ddof, numeric_only, **kwargs)
setattr(cls, "var", var)
@doc(
_num_ddof_doc,
desc="Return sample standard deviation over requested axis."
"\n\nNormalized by N-1 by default. This can be changed using the "
"ddof argument",
name1=name1,
name2=name2,
axis_descr=axis_descr,
)
def std(
self,
axis=None,
skipna=None,
level=None,
ddof=1,
numeric_only=None,
**kwargs,
):
return NDFrame.std(self, axis, skipna, level, ddof, numeric_only, **kwargs)
setattr(cls, "std", std)
@doc(
_cnum_doc,
desc="minimum",
name1=name1,
name2=name2,
axis_descr=axis_descr,
accum_func_name="min",
examples=_cummin_examples,
)
def cummin(self, axis=None, skipna=True, *args, **kwargs):
return NDFrame.cummin(self, axis, skipna, *args, **kwargs)
setattr(cls, "cummin", cummin)
@doc(
_cnum_doc,
desc="maximum",
name1=name1,
name2=name2,
axis_descr=axis_descr,
accum_func_name="max",
examples=_cummax_examples,
)
def cummax(self, axis=None, skipna=True, *args, **kwargs):
return NDFrame.cummax(self, axis, skipna, *args, **kwargs)
setattr(cls, "cummax", cummax)
@doc(
_cnum_doc,
desc="sum",
name1=name1,
name2=name2,
axis_descr=axis_descr,
accum_func_name="sum",
examples=_cumsum_examples,
)
def cumsum(self, axis=None, skipna=True, *args, **kwargs):
return NDFrame.cumsum(self, axis, skipna, *args, **kwargs)
setattr(cls, "cumsum", cumsum)
@doc(
_cnum_doc,
desc="product",
name1=name1,
name2=name2,
axis_descr=axis_descr,
accum_func_name="prod",
examples=_cumprod_examples,
)
def cumprod(self, axis=None, skipna=True, *args, **kwargs):
return NDFrame.cumprod(self, axis, skipna, *args, **kwargs)
setattr(cls, "cumprod", cumprod)
@doc(
_num_doc,
desc="Return the sum of the values over the requested axis.\n\n"
"This is equivalent to the method ``numpy.sum``.",
name1=name1,
name2=name2,
axis_descr=axis_descr,
min_count=_min_count_stub,
see_also=_stat_func_see_also,
examples=_sum_examples,
)
def sum(
self,
axis=None,
skipna=None,
level=None,
numeric_only=None,
min_count=0,
**kwargs,
):
return NDFrame.sum(
self, axis, skipna, level, numeric_only, min_count, **kwargs
)
setattr(cls, "sum", sum)
@doc(
_num_doc,
desc="Return the product of the values over the requested axis.",
name1=name1,
name2=name2,
axis_descr=axis_descr,
min_count=_min_count_stub,
see_also=_stat_func_see_also,
examples=_prod_examples,
)
def prod(
self,
axis=None,
skipna=None,
level=None,
numeric_only=None,
min_count=0,
**kwargs,
):
return NDFrame.prod(
self, axis, skipna, level, numeric_only, min_count, **kwargs
)
setattr(cls, "prod", prod)
cls.product = prod
@doc(
_num_doc,
desc="Return the mean of the values over the requested axis.",
name1=name1,
name2=name2,
axis_descr=axis_descr,
min_count="",
see_also="",
examples="",
)
def mean(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
return NDFrame.mean(self, axis, skipna, level, numeric_only, **kwargs)
setattr(cls, "mean", mean)
@doc(
_num_doc,
desc="Return unbiased skew over requested axis.\n\nNormalized by N-1.",
name1=name1,
name2=name2,
axis_descr=axis_descr,
min_count="",
see_also="",
examples="",
)
def skew(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
return NDFrame.skew(self, axis, skipna, level, numeric_only, **kwargs)
setattr(cls, "skew", skew)
@doc(
_num_doc,
desc="Return unbiased kurtosis over requested axis.\n\n"
"Kurtosis obtained using Fisher's definition of\n"
"kurtosis (kurtosis of normal == 0.0). Normalized "
"by N-1.",
name1=name1,
name2=name2,
axis_descr=axis_descr,
min_count="",
see_also="",
examples="",
)
def kurt(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
return NDFrame.kurt(self, axis, skipna, level, numeric_only, **kwargs)
setattr(cls, "kurt", kurt)
cls.kurtosis = kurt
@doc(
_num_doc,
desc="Return the median of the values over the requested axis.",
name1=name1,
name2=name2,
axis_descr=axis_descr,
min_count="",
see_also="",
examples="",
)
def median(
self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs
):
return NDFrame.median(self, axis, skipna, level, numeric_only, **kwargs)
setattr(cls, "median", median)
@doc(
_num_doc,
desc="Return the maximum of the values over the requested axis.\n\n"
"If you want the *index* of the maximum, use ``idxmax``. This is "
"the equivalent of the ``numpy.ndarray`` method ``argmax``.",
name1=name1,
name2=name2,
axis_descr=axis_descr,
min_count="",
see_also=_stat_func_see_also,
examples=_max_examples,
)
def max(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
return NDFrame.max(self, axis, skipna, level, numeric_only, **kwargs)
setattr(cls, "max", max)
@doc(
_num_doc,
desc="Return the minimum of the values over the requested axis.\n\n"
"If you want the *index* of the minimum, use ``idxmin``. This is "
"the equivalent of the ``numpy.ndarray`` method ``argmin``.",
name1=name1,
name2=name2,
axis_descr=axis_descr,
min_count="",
see_also=_stat_func_see_also,
examples=_min_examples,
)
def min(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
return NDFrame.min(self, axis, skipna, level, numeric_only, **kwargs)
setattr(cls, "min", min)