def argmin(self, axis=None):
"""
return a ndarray of the minimum argument indexer
See also
--------
numpy.ndarray.argmin
"""
return nanops.nanargmin(self.values)
def argmin(self, axis=None, skipna=True, *args, **kwargs) -> int:
delegate = self._values
nv.validate_minmax_axis(axis)
skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs)
if isinstance(delegate, ExtensionArray):
if not skipna and delegate.isna().any():
return -1
else:
return delegate.argmin()
else:
return nanops.nanargmin(delegate, skipna=skipna)
def argmin(self, axis=None, skipna=True, *args, **kwargs) -> int:
delegate = self._values
nv.validate_minmax_axis(axis)
skipna = nv.validate_argmin_with_skipna(skipna, args, kwargs)
if isinstance(delegate, ExtensionArray):
if not skipna and delegate.isna().any():
return -1
else:
return delegate.argmin()
else:
# error: Incompatible return value type (got "Union[int, ndarray]", expected
# "int")
return nanops.nanargmin( # type: ignore[return-value]
delegate, skipna=skipna)
def argmin(self, axis=None, skipna=True):
"""
Return a ndarray of the minimum argument indexer.
Parameters
----------
axis : {None}
Dummy argument for consistency with Series
skipna : bool, default True
See Also
--------
numpy.ndarray.argmin
"""
nv.validate_minmax_axis(axis)
return nanops.nanargmin(self._values, skipna=skipna)
def argmin(self, axis=None, skipna=True):
"""
Return a ndarray of the minimum argument indexer.
Parameters
----------
axis : {None}
Dummy argument for consistency with Series
skipna : bool, default True
See Also
--------
numpy.ndarray.argmin
"""
nv.validate_minmax_axis(axis)
return nanops.nanargmin(self._values, skipna=skipna)
def argmin(self, axis=None, skipna=True, *args, **kwargs):
"""
Return a ndarray of the minimum argument indexer.
Parameters
----------
axis : {None}
Dummy argument for consistency with Series.
skipna : bool, default True
Returns
-------
numpy.ndarray
See Also
--------
numpy.ndarray.argmin : Return indices of the minimum values along
the given axis.
"""
nv.validate_minmax_axis(axis)
nv.validate_argmax_with_skipna(skipna, args, kwargs)
return nanops.nanargmin(self._values, skipna=skipna)
def argmin(self, axis=None, skipna=True, *args, **kwargs) -> int:
nv.validate_minmax_axis(axis)
nv.validate_argmax_with_skipna(skipna, args, kwargs)
return nanops.nanargmin(self._values, skipna=skipna)
