Python | Pandas Series.update()
Last Updated :
29 Jan, 2019
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 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.update()
function modify Series in place using non-NA values from passed Series object. The function aligns on index.
Syntax: Series.update(other)
Parameter :
other: series
Returns : None
Example #1: Use Series.update()
function to update the values of some cities in the given Series object
import pandas as pd
sr = pd.Series([ 'New York' , 'Chicago' , None , 'Toronto' , 'Lisbon' , 'Rio' , 'Chicago' , 'Lisbon' ])
print (sr)
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Output :
Now we will use Series.update()
function to update the values identified the passed indexed in the given Series object.
sr.update(pd.Series([ 'Melbourne' , 'Moscow' ], index = [ 2 , 7 ]))
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Output :
As we can see in the output, the Series.update()
function has successfully updated the values in the original series object from the passed series object.
Example #2: Use Series.update()
function to update the values of some elements in the given Series object
import pandas as pd
sr = pd.Series([ 100 , 214 , 325 , 88 , None , 325 , None , 325 , 100 ])
print (sr)
|
Output :
Now we will use Series.update()
function to update the values identified the passed indexed in the given Series object.
sr.update(pd.Series([ 5000 , 6000 ], index = [ 4 , 6 ]))
|
Output :
As we can see in the output, the Series.update()
function has successfully updated the values in the original series object from the passed series object.
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