Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas Index.shift() function 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. This method is only implemented for datetime-like index classes, i.e., DatetimeIndex, PeriodIndex and TimedeltaIndex.
Syntax: Index.shift(periods=1, freq=None)
Parameters :
periods : 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’ etcReturns : shifted index
Example #1: Use Index.shift() function to shift a time-series data by certain duration.
# importing pandas as pd import pandas as pd # Creating the index idx = pd.date_range('1 / 1/2018', periods = 3, freq ='MS') # Print the index idx |
Output :
Now we would shift the index by 5 Days.
# shifting the index by 5 days idx.shift(5, freq ='D') |
Output :
As we can see in the output, the dates have been shifted forward by 5 Days.
Example #2: Use Index.shift() function shift the date-time based index.
# importing pandas as pd import pandas as pd # Creating the index idx = pd.date_range('1 / 1/2018', periods = 3, freq ='MS') # Print the index idx |
Output :
Now we would shift the index by 5 Months.
# shifting the index by 5 Months idx.shift(5, freq ='MS') |
Output :
As we can see in the output, the date has been shifted forward by 5 months.
