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 fillna() Syntax
Pandas Series.fillna() function is used to fill Pandas NA/NaN values using the specified method.
Syntax: Series.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)
Parameter :
- value : Value to use to fill holes
- method : Method to use for filling holes in reindexed Series pad / ffill
- 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
Pandas DataFrame fillna() Examples
Example 1: Use Series.fillna() function to fill out the missing values in the given series object. Use a dictionary to pass the values to be filled corresponding to the different index labels in the series object.
# importing pandas as pd import pandas as pd
# Creating the Series sr = pd.Series([ 'New York' , 'Chicago' , 'Toronto' , None , 'Rio' ])
# Create the Index sr.index = [ 'City 1' , 'City 2' , 'City 3' , 'City 4' , 'City 5' ]
# set the index sr.index = index_
# Print the series print (sr)
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Output :
Now we will use Series.fillna() function to fill out the missing values in the given series object.
# fill the values using dictionary result = sr.fillna(value = { 'City 4' : 'Lisbon' ,
'City 1' : 'Dublin' })
# Print the result print (result)
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Output :
As we can see in the output, the Series.fillna() function has successfully filled out the missing values in the given series object.
Example 2: Use Series.fillna() function to fill out the missing values in the given series object using forward fill (ffill) method.
# importing pandas as pd import pandas as pd
# Creating the Series sr = pd.Series([ 100 , None , None , 18 , 65 ,
None , 32 , 10 , 5 , 24 , None ])
# Create the Index index_ = pd.date_range( '2010-10-09' ,
periods = 11 , freq = 'M' )
# set the index sr.index = index_
# Print the series print (sr)
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Output :
Now we will use Series.fillna() function to fill out the missing values in the given series object. We will use forward fill method to fill out the missing values.
# fill the values using forward fill method result = sr.fillna(method = 'ffill' )
# Print the result print (result)
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Output :
As we can see in the output, the Series.fillna() function has successfully filled out the missing values in the given series object.