xorbits.pandas.DataFrame.skew

DataFrame.skew(axis=None, skipna=True, level=None, numeric_only=None, combine_size=None, bias=False, method=None)

Return unbiased skew over requested axis.

Normalized by N-1.

Parameters

• axis ({index (0), columns (1)}) –

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

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

Returns

• Series or scalar – .. rubric:: Examples

  >>> s = pd.Series([1, 2, 3])  
  >>> s.skew()  
  0.0
  

  With a DataFrame

  >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4], 'c': [1, 3, 5]},  
  ...                  index=['tiger', 'zebra', 'cow'])
  >>> df  
          a   b   c
  tiger   1   2   1
  zebra   2   3   3
  cow     3   4   5
  >>> df.skew()  
  a   0.0
  b   0.0
  c   0.0
  dtype: float64
  

  Using axis=1

  >>> df.skew(axis=1)  
  tiger   1.732051
  zebra  -1.732051
  cow     0.000000
  dtype: float64
  

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

  >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': ['T', 'Z', 'X']},  
  ...                  index=['tiger', 'zebra', 'cow'])
  >>> df.skew(numeric_only=True)  
  a   0.0
  dtype: float64
  

• This docstring was copied from pandas.core.frame.DataFrame.