Python | Pandas Series.truncate()
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.truncate()
function is used to truncate a Series or DataFrame before and after some index value. This is a useful shorthand for boolean indexing based on index values above or below certain thresholds.
Syntax: Series.truncate(before=None, after=None, axis=None, copy=True)
Parameter :
before : Truncate all rows before this index value.
after : Truncate all rows after this index value.
axis : Axis to truncate. Truncates the index (rows) by default.
copy : Return a copy of the truncated section.
Returns : truncated Series or DataFrame.
Example #1: Use Series.truncate()
function to truncate some data from the series prior to a given date.
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)
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Output :
Now we will use Series.truncate()
function to truncate data which are prior to ‘2014-08-17 10:00:00+02:00’ in the given Series object.
sr.truncate(before = '2014-08-17 10:00:00 + 02:00' )
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Output :
As we can see in the output, the Series.truncate()
function has successfully truncated all data prior to the mentioned date.
Example #2: Use Series.truncate()
function to truncate some data from the series prior to a given index label and after a given index label.
import pandas as pd
sr = pd.Series([ 19.5 , 16.8 , 22.78 , 20.124 , 18.1002 ])
print (sr)
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Output :
Now we will use Series.truncate()
function to truncate data which are prior to the 1st index label and after the 3rd index label in the given Series object.
sr.truncate(before = 1 , after = 3 )
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Output :
As we can see in the output, the Series.truncate()
function has successfully truncated all data prior to the mentioned index label and after the mentioned index label.
Last Updated :
05 Feb, 2019
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