@@ -906,21 +906,23 @@ def skew(self, **kwargs):
906906
907907 Parameters
908908 ----------
909- kwargs : Under Review
909+ **kwargs
910+ Under Review.
910911
911912 Returns
912913 -------
913- Series or DataFrame (matches input)
914- Like-indexed object containing the result of function application
914+ Series or DataFrame
915+ Returned object type is determined by the caller of the %(name)s
916+ calculation
915917
916918 See Also
917919 --------
918- pandas. Series.%(name)s
919- pandas. DataFrame.%(name)s
920- pandas. Series.kurtosis
921- pandas. DataFrame.kurtosis
922- scipy.stats.skew
923- scipy.stats.kurtosis
920+ Series.%(name)s : Calling object with Series data
921+ DataFrame.%(name)s : Calling object with DataFrames
922+ Series.kurt : Equivalent method for Series
923+ DataFrame.kurt : Equivalent method for DataFrame
924+ scipy.stats.skew : Third moment of a probability density
925+ scipy.stats.kurtosis : Reference SciPy method
924926
925927 Notes
926928 -----
@@ -932,19 +934,20 @@ def skew(self, **kwargs):
932934 four matching the equivalent function call using `scipy.stats`.
933935
934936 >>> arr = [1, 2, 3, 4, 999]
937+ >>> fmt = "{0:.6f}" # limit the printed precision to 6 digits
935938 >>> import scipy.stats
936- >>> print("{0:.6f}" .format(scipy.stats.kurtosis(arr[:-1], bias=False)))
939+ >>> print(fmt .format(scipy.stats.kurtosis(arr[:-1], bias=False)))
937940 -1.200000
938- >>> print("{0:.6f}" .format(scipy.stats.kurtosis(arr[1:], bias=False)))
941+ >>> print(fmt .format(scipy.stats.kurtosis(arr[1:], bias=False)))
939942 3.999946
940- >>> df = pd.DataFrame (arr)
941- >>> df .rolling(4).kurt()
942- 0
943- 0 NaN
944- 1 NaN
945- 2 NaN
946- 3 -1.200000
947- 4 3.999946
943+ >>> s = pd.Series (arr)
944+ >>> s .rolling(4).kurt()
945+ 0 NaN
946+ 1 NaN
947+ 2 NaN
948+ 3 -1.200000
949+ 4 3.999946
950+ dtype: float64
948951 """ )
949952
950953 def kurt (self , ** kwargs ):
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