oupby.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.skew.html)
    * [ 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.DataFrameGroupBy.skew  #

DataFrameGroupBy.  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#L2625-L2719) #

    

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.

Specifying ` axis=None  ` will apply the aggregation across both axes.

New in version 2.0.0.

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.

****kwargs**

    

Additional keyword arguments to be passed to the function.

Returns  :

    

DataFrame

    

See also

` DataFrame.skew  `

    

Return unbiased skew over requested axis.

Examples

    
    
    >>> arrays = [['falcon', 'parrot', 'cockatoo', 'kiwi',
    ...            'lion', 'monkey', 'rabbit'],
    ...           ['bird', 'bird', 'bird', 'bird',
    ...            'mammal', 'mammal', 'mammal']]
    >>> index = pd.MultiIndex.from_arrays(arrays, names=('name', 'class'))
    >>> df = pd.DataFrame({'max_speed': [389.0, 24.0, 70.0, np.nan,
    ...                                  80.5, 21.5, 15.0]},
    ...                   index=index)
    >>> df
                    max_speed
    name     class
    falcon   bird        389.0
    parrot   bird         24.0
    cockatoo bird         70.0
    kiwi     bird          NaN
    lion     mammal       80.5
    monkey   mammal       21.5
    rabbit   mammal       15.0
    >>> gb = df.groupby(["class"])
    >>> gb.skew()
            max_speed
    class
    bird     1.628296
    mammal   1.669046
    >>> gb.skew(skipna=