def mean(self,
axis=None,
dtype=None,
out=None,
keepdims=False,
skipna=True):
nv.validate_mean((), dict(dtype=dtype, out=out, keepdims=keepdims))
return nanops.nanmean(self._ndarray, axis=axis, skipna=skipna)
def mean(self,
*,
axis=None,
dtype=None,
out=None,
keepdims=False,
skipna=True):
nv.validate_mean((), dict(dtype=dtype, out=out, keepdims=keepdims))
result = nanops.nanmean(self._ndarray, axis=axis, skipna=skipna)
return self._wrap_reduction_result(axis, result)
def mean(
self,
*,
axis=None,
dtype: Optional[NpDtype] = None,
out=None,
keepdims=False,
skipna=True,
):
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 mean(self, axis=0, *args, **kwargs):
"""
Mean of non-NA/null values
Returns
-------
mean : float
"""
nv.validate_mean(args, kwargs)
valid_vals = self._valid_sp_values
sp_sum = valid_vals.sum()
ct = len(valid_vals)
if self._null_fill_value:
return sp_sum / ct
else:
nsparse = self.sp_index.ngaps
return (sp_sum + self.fill_value * nsparse) / (ct + nsparse)
def mean(self, axis=0, *args, **kwargs):
"""
Mean of non-NA/null values
Returns
-------
mean : float
"""
nv.validate_mean(args, kwargs)
valid_vals = self._valid_sp_values
sp_sum = valid_vals.sum()
ct = len(valid_vals)
if self._null_fill_value:
return sp_sum / ct
else:
nsparse = self.sp_index.ngaps
return (sp_sum + self.fill_value * nsparse) / (ct + nsparse)
def mean(self, axis=None, dtype=None, out=None, keepdims=False,
skipna=True):
nv.validate_mean((), dict(dtype=dtype, out=out, keepdims=keepdims))
return nanops.nanmean(self._ndarray, axis=axis, skipna=skipna)
