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.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.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.var  #

SeriesGroupBy.  var  (  _ ddof  =  1  _ , _ engine  =  None  _ , _
engine_kwargs  =  None  _ , _ numeric_only  =  False  _ )  [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.2/pandas/core/groupby/groupby.py#L2648-L2753) #

    

Compute variance of groups, excluding missing values.

For multiple groupings, the result index will be a MultiIndex.

Parameters  :

    

**ddof** int, default 1

    

Degrees of freedom.

**engine** str, default None

    

  * ` 'cython'  ` : Runs the operation through C-extensions from cython. 

  * ` 'numba'  ` : Runs the operation through JIT compiled code from numba. 

  * ` None  ` : Defaults to ` 'cython'  ` or globally setting ` compute.use_numba  `

New in version 1.4.0.

**engine_kwargs** dict, default None

    

  * For ` 'cython'  ` engine, there are no accepted ` engine_kwargs  `

  * For ` 'numba'  ` engine, the engine can accept ` nopython  ` , ` nogil  ` and ` parallel  ` dictionary keys. The values must either be ` True  ` or ` False  ` . The default ` engine_kwargs  ` for the ` 'numba'  ` engine is ` {{'nopython':  True,  'nogil':  False,  'parallel':  False}}  `

New in version 1.4.0.

**numeric_only** bool, default False

    

Include only  float  ,  int  or  boolean  data.

New in version 1.5.0.

Changed in version 2.0.0:  numeric_only now defaults to ` False  ` .

Returns  :

    

Series or DataFrame

    

Variance of values within each group.

See also

` Series.groupby  `

    

Apply a function groupby to a Series.

` DataFrame.groupby  `

    

Apply a function groupby to each row or column of a DataFrame.

Examples

For SeriesGroupBy:

    
    
    >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
    >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst)
    >>> ser
    a     7
    a     2
    a     8
    b     4
    b     3
    b     3
    dtype: int64
    >>> ser.groupby(level=0).var()
    a    10.333333
    b     0.333333
    dtype: float64