Python | Pandas Series.slice_shift()
Last Updated :
05 Feb, 2019
Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.
Pandas Series.slice_shift()
function is equivalent to shift without copying data. The shifted data will not include the dropped periods and the shifted axis will be smaller than the original.
Syntax: Series.slice_shift(periods=1, axis=0)
Parameter :
periods : Number of periods to move, can be positive or negative
Returns : shifted : same type as caller
Example #1: Use Series.slice_shift()
function to shift the data of the given Series object by 2 periods.
import pandas as pd
sr = pd.Series([ 'New York' , 'Chicago' , 'Toronto' , 'Lisbon' , 'Rio' , 'Moscow' ])
didx = pd.DatetimeIndex(start = '2014-08-01 10:00' , freq = 'W' ,
periods = 6 , tz = 'Europe/Berlin' )
sr.index = didx
print (sr)
|
Output :
Now we will use Series.slice_shift()
function to shift the data in the given series object by 2 periods.
sr.slice_shift(periods = 2 )
|
Output :
As we can see in the output, the Series.slice_shift()
function has successfully shifted the data over the index. Notice the first two index labels are dropped.
Example #2: Use Series.slice_shift()
function to shift the data of the given Series object by -2 periods.
import pandas as pd
sr = pd.Series([ 'New York' , 'Chicago' , 'Toronto' , 'Lisbon' , 'Rio' , 'Moscow' ])
didx = pd.DatetimeIndex(start = '2014-08-01 10:00' , freq = 'W' ,
periods = 6 , tz = 'Europe/Berlin' )
sr.index = didx
print (sr)
|
Output :
Now we will use Series.slice_shift()
function to shift the data in the given series object by -2 periods.
sr.slice_shift(periods = - 2 )
|
Output :
As we can see in the output, the Series.slice_shift()
function has successfully shifted the data over the index. Notice the last two index labels are dropped and the data has been shifted upward.
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