parameters: (self, axis=0, dropna=True)
return_statement: return self.apply(Series.nunique, axis=axis, dropna=dropna)
docstring: Count distinct observations over requested axis.
  
          Return Series with number of distinct observations. Can ignore NaN
          values.
  
          Parameters
          ----------
          axis : {0 or 'index', 1 or 'columns'}, default 0
              The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for
              column-wise.
          dropna : bool, default True
              Don't include NaN in the counts.
  
          Returns
          -------
          Series
  
          See Also
          --------
          Series.nunique: Method nunique for Series.
          DataFrame.count: Count non-NA cells for each column or row.
  
          Examples
          --------
          >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [1, 1, 1]})
          >>> df.nunique()
          A    3
          B    1
          dtype: int64
  
          >>> df.nunique(axis=1)
          0    1
          1    2
          2    2
          dtype: int64
docstring_summary: Count distinct observations over requested axis.
docstring_tokens: Count distinct observations over requested axis .
function: def nunique(self, axis=0, dropna=True) -> Series:
          """
          Count distinct observations over requested axis.
  
          Return Series with number of distinct observations. Can ignore NaN
          values.
  
          Parameters
          ----------
          axis : {0 or 'index', 1 or 'columns'}, default 0
              The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for
              column-wise.
          dropna : bool, default True
              Don't include NaN in the counts.
  
          Returns
          -------
          Series
  
          See Also
          --------
          Series.nunique: Method nunique for Series.
          DataFrame.count: Count non-NA cells for each column or row.
  
          Examples
          --------
          >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [1, 1, 1]})
          >>> df.nunique()
          A    3
          B    1
          dtype: int64
  
          >>> df.nunique(axis=1)
          0    1
          1    2
          2    2
          dtype: int64
          """
          return self.apply(Series.nunique, axis=axis, dropna=dropna)
function_tokens: def nunique ( self , axis = 0 , dropna = True ) -> Series : return self . apply ( Series . nunique , axis = axis , dropna = dropna )