pandas.Index.shift¶

Index.shift(periods=1, freq=None)[source]¶

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