xorbits.pandas.Series.mean

Series.mean(axis=None, skipna=True, level=None, combine_size=None, method=None, **kwargs)

Return the mean of the values over the requested axis.

Parameters

• axis ({index (0)}) –

  Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

  For DataFrames, specifying axis=None will apply the aggregation across both axes.

  New in version 2.0.0(pandas).

• skipna (bool, default True) – Exclude NA/null values when computing the result.

• numeric_only (bool, default False (Not supported yet)) – Include only float, int, boolean columns. Not implemented for Series.

• **kwargs – Additional keyword arguments to be passed to the function.

Returns

• scalar or scalar – .. rubric:: Examples

  >>> s = pd.Series([1, 2, 3])  
  >>> s.mean()  
  2.0
  

  With a DataFrame

  >>> df = pd.DataFrame({'a': [1, 2], 'b': [2, 3]}, index=['tiger', 'zebra'])  
  >>> df  
         a   b
  tiger  1   2
  zebra  2   3
  >>> df.mean()  
  a   1.5
  b   2.5
  dtype: float64
  

  Using axis=1

  >>> df.mean(axis=1)  
  tiger   1.5
  zebra   2.5
  dtype: float64
  

  In this case, numeric_only should be set to True to avoid getting an error.

  >>> df = pd.DataFrame({'a': [1, 2], 'b': ['T', 'Z']},  
  ...                   index=['tiger', 'zebra'])
  >>> df.mean(numeric_only=True)  
  a   1.5
  dtype: float64
  

• This docstring was copied from pandas.core.series.Series.