Pandas Series.fillna() Method

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. 

Python3

# 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)

Output : 

Now we will use Series.fillna() function to fill out the missing values in the given series object. 

Python3

# fill the values using dictionary
result = sr.fillna(value={'City 4': 'Lisbon',
                          'City 1': 'Dublin'})

# Print the result
print(result)

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. 

Python3

# 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)

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. 

Python3

# fill the values using forward fill method
result = sr.fillna(method = 'ffill')

# Print the result
print(result)

Output : 

 As we can see in the output, the Series.fillna() function has successfully filled out the missing values in the given series object.