pandas.Index.shift — pandas 0.24.0rc1 documentation (original) (raw)
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: | 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. |
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| Returns: | pandas.Index shifted index |
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')