SeriesGroupBy.resample.html)
    * [ pandas.core.groupby.SeriesGroupBy.rolling ](pandas.core.groupby.SeriesGroupBy.rolling.html)
    * [ pandas.core.groupby.SeriesGroupBy.sample ](pandas.core.groupby.SeriesGroupBy.sample.html)
    * [ pandas.core.groupby.SeriesGroupBy.sem ](pandas.core.groupby.SeriesGroupBy.sem.html)
    * [ pandas.core.groupby.SeriesGroupBy.shift ](pandas.core.groupby.SeriesGroupBy.shift.html)
    * [ pandas.core.groupby.SeriesGroupBy.size ](pandas.core.groupby.SeriesGroupBy.size.html)
    * pandas.core.groupby.SeriesGroupBy.skew 
    * [ pandas.core.groupby.SeriesGroupBy.std ](pandas.core.groupby.SeriesGroupBy.std.html)
    * [ pandas.core.groupby.SeriesGroupBy.sum ](pandas.core.groupby.SeriesGroupBy.sum.html)
    * [ pandas.core.groupby.SeriesGroupBy.var ](pandas.core.groupby.SeriesGroupBy.var.html)
    * [ pandas.core.groupby.SeriesGroupBy.tail ](pandas.core.groupby.SeriesGroupBy.tail.html)
    * [ pandas.core.groupby.SeriesGroupBy.take ](pandas.core.groupby.SeriesGroupBy.take.html)
    * [ pandas.core.groupby.SeriesGroupBy.value_counts ](pandas.core.groupby.SeriesGroupBy.value_counts.html)
    * [ pandas.core.groupby.DataFrameGroupBy.boxplot ](pandas.core.groupby.DataFrameGroupBy.boxplot.html)
    * [ pandas.core.groupby.DataFrameGroupBy.hist ](pandas.core.groupby.DataFrameGroupBy.hist.html)
    * [ pandas.core.groupby.SeriesGroupBy.hist ](pandas.core.groupby.SeriesGroupBy.hist.html)
    * [ pandas.core.groupby.DataFrameGroupBy.plot ](pandas.core.groupby.DataFrameGroupBy.plot.html)
    * [ pandas.core.groupby.SeriesGroupBy.plot ](pandas.core.groupby.SeriesGroupBy.plot.html)
  * [ Resampling ](../resampling.html)
  * [ Style ](../style.html)
  * [ Plotting ](../plotting.html)
  * [ Options and settings ](../options.html)
  * [ Extensions ](../extensions.html)
  * [ Testing ](../testing.html)
  * [ Missing values ](../missing_value.html)

  * [ __ ](../../index.html)
  * [ API reference ](../index.html)
  * [ GroupBy ](../groupby.html)
  * pandas.core.... 

#  pandas.core.groupby.SeriesGroupBy.skew  #

SeriesGroupBy.  skew  (  _ axis  =  _NoDefault.no_default  _ , _ skipna  =
True  _ , _ numeric_only  =  False  _ , _ **  kwargs  _ )  [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/generic.py#L1063-L1146) #

    

Return unbiased skew within groups.

Normalized by N-1.

Parameters  :

    

**axis** {0 or âindexâ, 1 or âcolumnsâ, None}, default 0

    

Axis for the function to be applied on. This parameter is only for
compatibility with DataFrame and is unused.

Deprecated since version 2.1.0:  For axis=1, operate on the underlying object
instead. Otherwise the axis keyword is not necessary.

**skipna** bool, default True

    

Exclude NA/null values when computing the result.

**numeric_only** bool, default False

    

Include only float, int, boolean columns. Not implemented for Series.

****kwargs**

    

Additional keyword arguments to be passed to the function.

Returns  :

    

Series

    

See also

` Series.skew  `

    

Return unbiased skew over requested axis.

Examples

    
    
    >>> ser = pd.Series([390., 350., 357., np.nan, 22., 20., 30.],
    ...                 index=['Falcon', 'Falcon', 'Falcon', 'Falcon',
    ...                        'Parrot', 'Parrot', 'Parrot'],
    ...                 name="Max Speed")
    >>> ser
    Falcon    390.0
    Falcon    350.0
    Falcon    357.0
    Falcon      NaN
    Parrot     22.0
    Parrot     20.0
    Parrot     30.0
    Name: Max Speed, dtype: float64
    >>> ser.groupby(level=0).skew()
    Falcon    1.525174
    Parrot    1.457863
    Name: Max Speed, dtype: float64
    >>> ser.groupby(level=0).skew(skipna=False)
    Falcon         NaN
    Parrot    1.457863
    Name: Max Speed, dtype: float64
    

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