Python | Pandas Series.ffill()
Last Updated :
13 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.ffill()
function is synonym for forward fill. This function is used t fill the missing values in the given series object using forward fill method.
Syntax: Series.ffill(axis=None, inplace=False, limit=None, downcast=None)
Parameter :
axis : {0 or ‘index’}
inplace : If True, fill in place.
limit : If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill
downcast : dict, default is None
Returns : filled : Series
Example #1: Use Series.ffill()
function to fill out the missing values in the given series object.
import pandas as pd
sr = pd.Series([ 'New York' , 'Chicago' , 'Toronto' , None , 'Rio' ])
sr.index = [ 'City 1' , 'City 2' , 'City 3' , 'City 4' , 'City 5' ]
sr.index = index_
print (sr)
|
Output :
Now we will use Series.ffill()
function to fill out the missing values in the given series object.
result = sr.ffill()
print (result)
|
Output :
As we can see in the output, the Series.ffill()
function has successfully filled out the missing values in the given series object.
Example #2 : Use Series.ffill()
function to fill out 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.ffill()
function to fill out the missing values in the given series object.
result = sr.ffill()
print (result)
|
Output :
As we can see in the output, the Series.ffill()
function has successfully filled out the missing values in the given series object.
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