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Removed unnecessary params in cum_func #13550

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Removed unnecessary params in cum_func #13550

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20 changes: 18 additions & 2 deletions pandas/compat/numpy/function.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@
from numpy import ndarray
from pandas.util.validators import (validate_args, validate_kwargs,
validate_args_and_kwargs)
from pandas.core.common import is_integer, UnsupportedFunctionCall
from pandas.core.common import is_bool, is_integer, UnsupportedFunctionCall
from pandas.compat import OrderedDict


Expand Down Expand Up @@ -148,10 +148,26 @@ def validate_clip_with_axis(axis, args, kwargs):
CUM_FUNC_DEFAULTS = OrderedDict()
CUM_FUNC_DEFAULTS['dtype'] = None
CUM_FUNC_DEFAULTS['out'] = None
validate_cum_func = CompatValidator(CUM_FUNC_DEFAULTS, method='kwargs')
validate_cum_func = CompatValidator(CUM_FUNC_DEFAULTS, method='both',
max_fname_arg_count=1)
validate_cumsum = CompatValidator(CUM_FUNC_DEFAULTS, fname='cumsum',
method='both', max_fname_arg_count=1)


def validate_cum_func_with_skipna(skipna, args, kwargs, name):
"""
If this function is called via the 'numpy' library, the third
parameter in its signature is 'dtype', which takes either a
'numpy' dtype or 'None', so check if the 'skipna' parameter is
a boolean or not
"""
if not is_bool(skipna):
args = (skipna,) + args
skipna = True

validate_cum_func(args, kwargs, fname=name)
return skipna

LOGICAL_FUNC_DEFAULTS = dict(out=None)
validate_logical_func = CompatValidator(LOGICAL_FUNC_DEFAULTS, method='kwargs')

Expand Down
4 changes: 2 additions & 2 deletions pandas/core/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5484,8 +5484,8 @@ def _make_cum_function(cls, name, name1, name2, axis_descr, desc, accum_func,
axis_descr=axis_descr)
@Appender("Return cumulative {0} over requested axis.".format(name) +
_cnum_doc)
def cum_func(self, axis=None, dtype=None, out=None, skipna=True, **kwargs):
nv.validate_cum_func(tuple(), kwargs, fname=name)
def cum_func(self, axis=None, skipna=True, *args, **kwargs):
skipna = nv.validate_cum_func_with_skipna(skipna, args, kwargs, name)
if axis is None:
axis = self._stat_axis_number
else:
Expand Down