dask.dataframe.Index.shift
dask.dataframe.Index.shift¶
- Index.shift(periods=1, freq=None)[source]¶
Shift index by desired number of time frequency increments.
This docstring was copied from pandas.core.indexes.base.Index.shift.
Some inconsistencies with the Dask version may exist.
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')