Replace NaN with given string in DataFrame in Pandas

This tutorial will discuss about different ways to replace NaN with a given string in Pandas DataFrame.

Table Of Contents

Preparing DataSet

Let’s create a DataFrame with four columns, and six rows. This DataFrame will certain NaN values.

import pandas as pd
import numpy as np

# List of Tuples
employees= [('Mark',   'US',    'Tech',    5),
            ('Riti',   'India', 'Tech' ,   7),
            (np.NaN,   np.NaN,  'PMO' ,    np.NaN),
            ('Shreya', 'India', 'Design',  2),
            (np.NaN,   'US',     np.NaN,   11),
            ('Sim',    np.NaN,    np.NaN,   4)]

# Create a DataFrame object from list of tuples
df = pd.DataFrame(employees,
                  columns=['Name', 'Location', 'Team', 'Experience'])

print(df)

Output

     Name Location    Team  Experience
0    Mark       US    Tech         5.0
1    Riti    India    Tech         7.0
2     NaN      NaN     PMO         NaN
3  Shreya    India  Design         2.0
4     NaN       US     NaN        11.0
5     Sim      NaN     NaN         4.0

Now we want to replace the NaN values in all the columns of this DataFrame with the a given string. There are different ways to do this. Let’s discuss them one by one.

Replace NaN with a string using fillna()

In Pandas, a DataFrame has a function fillna(value), to replace all NaN values in the DataFrame with the given value. To replace all NaNs with a string, call the fillna() function, and pass the string as value parameter in it. Also, pass the inplace=True as the second argument in the fillna(). It will modify the DataFrame in place.

replacementStr = "Missing"

# Replace NaN with the given in whole DataFrame
df.fillna(value=replacementStr, inplace=True)

print(df)

Output

      Name Location     Team Experience
0     Mark       US     Tech        5.0
1     Riti    India     Tech        7.0
2  Missing  Missing      PMO    Missing
3   Shreya    India   Design        2.0
4  Missing       US  Missing       11.0
5      Sim  Missing  Missing        4.0

It replaced all the NaN values with a string “Mising” in all the columns of DataFrame.

Replace NaN with a string using replace()

Pandas DataFrame provides a function replace(), to replace all the occurrences of a given value with a replacemenet value. To replace all occurrences of NaN with a string, pass both the NaN and replacement string as arguments in the replace() function. Also, pass inplace as True, due to which all modifications in DataFrame will be in place.

replacementStr = "Missing"

# Replace NaN with a string in whole DataFrame
df.replace(np.NaN,  replacementStr, inplace=True)

print(df)

Output

      Name Location     Team Experience
0     Mark       US     Tech        5.0
1     Riti    India     Tech        7.0
2  Missing  Missing      PMO    Missing
3   Shreya    India   Design        2.0
4  Missing       US  Missing       11.0
5      Sim  Missing  Missing        4.0

It replaced all the NaN values with the string “Missing” in all the columns of DataFrame.

Summary

We learned two different ways to replace NaN with a given string in a complete DataFrame in Pandas.