parameters: (self, periods=1, freq=None)
docstring: Shift index by desired number of time frequency increments.
  
          This method is for shifting the values of datetime-like indexes
          by a specified time increment a given number of times.
  
          Parameters
          ----------
          periods : int, default 1
              Number of periods (or increments) to shift by,
              can be positive or negative.
          freq : pandas.DateOffset, pandas.Timedelta or str, optional
              Frequency increment to shift by.
              If None, the index is shifted by its own `freq` attribute.
              Offset aliases are valid strings, e.g., 'D', 'W', 'M' etc.
  
          Returns
          -------
          pandas.Index
              Shifted index.
  
          See Also
          --------
          Series.shift : Shift values of Series.
  
          Notes
          -----
          This method is only implemented for datetime-like index classes,
          i.e., DatetimeIndex, PeriodIndex and TimedeltaIndex.
  
          Examples
          --------
          Put the first 5 month starts of 2011 into an index.
  
          >>> month_starts = pd.date_range('1/1/2011', periods=5, freq='MS')
          >>> month_starts
          DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01', '2011-04-01',
                         '2011-05-01'],
                        dtype='datetime64[ns]', freq='MS')
  
          Shift the index by 10 days.
  
          >>> month_starts.shift(10, freq='D')
          DatetimeIndex(['2011-01-11', '2011-02-11', '2011-03-11', '2011-04-11',
                         '2011-05-11'],
                        dtype='datetime64[ns]', freq=None)
  
          The default value of `freq` is the `freq` attribute of the index,
          which is 'MS' (month start) in this example.
  
          >>> month_starts.shift(10)
          DatetimeIndex(['2011-11-01', '2011-12-01', '2012-01-01', '2012-02-01',
                         '2012-03-01'],
                        dtype='datetime64[ns]', freq='MS')
docstring_summary: Shift index by desired number of time frequency increments.
docstring_tokens: Shift index by desired number of time frequency increments .
function: def shift(self, periods=1, freq=None):
          """
          Shift index by desired number of time frequency increments.
  
          This method is for shifting the values of datetime-like indexes
          by a specified time increment a given number of times.
  
          Parameters
          ----------
          periods : int, default 1
              Number of periods (or increments) to shift by,
              can be positive or negative.
          freq : pandas.DateOffset, pandas.Timedelta or str, optional
              Frequency increment to shift by.
              If None, the index is shifted by its own `freq` attribute.
              Offset aliases are valid strings, e.g., 'D', 'W', 'M' etc.
  
          Returns
          -------
          pandas.Index
              Shifted index.
  
          See Also
          --------
          Series.shift : Shift values of Series.
  
          Notes
          -----
          This method is only implemented for datetime-like index classes,
          i.e., DatetimeIndex, PeriodIndex and TimedeltaIndex.
  
          Examples
          --------
          Put the first 5 month starts of 2011 into an index.
  
          >>> month_starts = pd.date_range('1/1/2011', periods=5, freq='MS')
          >>> month_starts
          DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01', '2011-04-01',
                         '2011-05-01'],
                        dtype='datetime64[ns]', freq='MS')
  
          Shift the index by 10 days.
  
          >>> month_starts.shift(10, freq='D')
          DatetimeIndex(['2011-01-11', '2011-02-11', '2011-03-11', '2011-04-11',
                         '2011-05-11'],
                        dtype='datetime64[ns]', freq=None)
  
          The default value of `freq` is the `freq` attribute of the index,
          which is 'MS' (month start) in this example.
  
          >>> month_starts.shift(10)
          DatetimeIndex(['2011-11-01', '2011-12-01', '2012-01-01', '2012-02-01',
                         '2012-03-01'],
                        dtype='datetime64[ns]', freq='MS')
          """
          raise NotImplementedError(f"Not supported for type {type(self).__name__}")
function_tokens: def shift ( self , periods = 1 , freq = None ) : raise NotImplementedError ( f"Not supported for type {type(self).__name__}" )