nankurt(values: 'np.ndarray', *, axis: 'int | None' = None, skipna: 'bool' = True, mask: 'npt.NDArray[np.bool_] | None' = None) -> 'float'
The statistic computed here is the adjusted Fisher-Pearson standardized moment coefficient G2, computed directly from the second and fourth central moment.
nan-mask if known
Unless input is a float array, in which case use the same precision as the input array.
Compute the sample excess kurtosis
>>> import pandas.core.nanops as nanopsSee :
... s = pd.Series([1, np.nan, 1, 3, 2])
... nanops.nankurt(s) -1.2892561983471076
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.nanops.nankurt
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