Python | Pandas Series.bfill()
Last Updated :
17 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.bfill()
function is synonym for the backward fill method. This function is used to fill the missing values in the given series object.
Syntax: Series.bfill(axis=None, inplace=False, limit=None, downcast=None)
Parameter :
axis : axis = 1
inplace : make changes to the same object
limit : maximum number of consecutive missing values to fill
Returns : Series
Example #1: Use Series.bfill()
function to fill the missing values in the given series object.
import pandas as pd
sr = pd.Series([ 'New York' , 'Chicago' , 'Toronto' , None , 'Rio' ])
index_ = [ 'City 1' , 'City 2' , 'City 3' , 'City 4' , 'City 5' ]
sr.index = index_
print (sr)
|
Output :
Now we will use Series.bfill()
function to fill the missing values in the given series object.
result = sr.bfill()
print (result)
|
Output :
As we can see in the output, the Series.bfill()
function has successfully filled the missing values in the given series object using the backward fill method.
Example #2 : Use Series.bfill()
function to fill the missing values in the given series object.
import pandas as pd
sr = pd.Series([ 100 , None , None , 18 , 65 , None , 32 , 10 , 5 , 24 , None ])
index_ = pd.date_range( '2010-10-09' , periods = 11 , freq = 'M' )
sr.index = index_
print (sr)
|
Output :
Now we will use Series.bfill()
function to fill the missing values in the given series object.
result = sr.bfill()
print (result)
|
Output :
As we can see in the output, the Series.bfill()
function has successfully filled the missing values in the given series object using the backward fill method. Notice the last value has not been filled because there is no valid value in the series after that element.
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