Python | Pandas Series.ffill()

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. 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.ffill() function to fill out the missing values in the given series object. Python3 1==

# fill the missing values
result = sr.ffill()

# Print the result
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. 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.ffill() function to fill out the missing values in the given series object. Python3 1==

# fill the missing values
result = sr.ffill()

# Print the result
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.