xorbits.pandas.Index.shift#

Index.shift(periods: int = 1, freq=None)[源代码]#

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.

参数

periods (int, default 1) – Number of periods (or increments) to shift by, can be positive or negative.
freq (pandas.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.

返回

Shifted index.

返回类型

pandas.Index

参见

Series.shift: Shift values of Series.

提示

This method is only implemented for datetime-like index classes, i.e., DatetimeIndex, PeriodIndex and TimedeltaIndex.

实际案例

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')

警告

This method has not been implemented yet. Xorbits will try to execute it with pandas.

This docstring was copied from pandas.core.indexes.base.Index.