def shift(self, periods=<NUM_LIT:1>, freq=None):
raise NotImplementedError("<STR_LIT>" %<EOL>type(self).__name__)<EOL>
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 string, 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')