pandas.Index.shift

Index.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

periodsint, default 1

Number of periods (or increments) to shift by, can be positive or negative.

freqpandas.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')

pandas.Index.set_value pandas.Index.slice_indexer