xorbits.pandas.DataFrame.sem

DataFrame.sem(axis=None, skipna=True, level=None, ddof=1, numeric_only=None, combine_size=None, method=None)

Return unbiased standard error of the mean over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters

• axis ({index (0), columns (1)}) – For Series this parameter is unused and defaults to 0.

• skipna (bool, default True) – Exclude NA/null values. If an entire row/column is NA, the result will be NA.

• ddof (int, default 1) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

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

Returns

• Series or DataFrame (if level specified) – .. rubric:: Examples

  >>> s = pd.Series([1, 2, 3])  
  >>> s.sem().round(6)  
  0.57735
  

  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.sem()  
  a   0.5
  b   0.5
  dtype: float64
  

  Using axis=1

  >>> df.sem(axis=1)  
  tiger   0.5
  zebra   0.5
  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.sem(numeric_only=True)  
  a   0.5
  dtype: float64
  

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