@@ -6895,14 +6895,61 @@ def skew(
6895
6895
)
6896
6896
6897
6897
@deprecate_nonkeyword_arguments (version = "3.0" , allowed_args = ["self" ], name = "kurt" )
6898
- @doc (make_doc ("kurt" , ndim = 1 ))
6899
6898
def kurt (
6900
6899
self ,
6901
6900
axis : Axis | None = 0 ,
6902
6901
skipna : bool = True ,
6903
6902
numeric_only : bool = False ,
6904
6903
** kwargs ,
6905
6904
):
6905
+ """
6906
+ Return unbiased kurtosis over requested axis.
6907
+
6908
+ Kurtosis obtained using Fisher's definition of
6909
+ kurtosis (kurtosis of normal == 0.0). Normalized by N-1.
6910
+
6911
+ Parameters
6912
+ ----------
6913
+ axis : {index (0)}
6914
+ Axis for the function to be applied on.
6915
+ For `Series` this parameter is unused and defaults to 0.
6916
+
6917
+ For DataFrames, specifying ``axis=None`` will apply the aggregation
6918
+ across both axes.
6919
+
6920
+ .. versionadded:: 2.0.0
6921
+
6922
+ skipna : bool, default True
6923
+ Exclude NA/null values when computing the result.
6924
+ numeric_only : bool, default False
6925
+ Include only float, int, boolean columns.
6926
+
6927
+ **kwargs
6928
+ Additional keyword arguments to be passed to the function.
6929
+
6930
+ Returns
6931
+ -------
6932
+ scalar
6933
+ Unbiased kurtosis.
6934
+
6935
+ See Also
6936
+ --------
6937
+ Series.skew : Return unbiased skew over requested axis.
6938
+ Series.var : Return unbiased variance over requested axis.
6939
+ Series.std : Return unbiased standard deviation over requested axis.
6940
+
6941
+ Examples
6942
+ --------
6943
+ >>> s = pd.Series([1, 2, 2, 3], index=["cat", "dog", "dog", "mouse"])
6944
+ >>> s
6945
+ cat 1
6946
+ dog 2
6947
+ dog 2
6948
+ mouse 3
6949
+ dtype: int64
6950
+ >>> s.kurt()
6951
+ 1.5
6952
+ """
6906
6953
return NDFrame .kurt (
6907
6954
self , axis = axis , skipna = skipna , numeric_only = numeric_only , ** kwargs
6908
6955
)
0 commit comments