Replace NaN with None in Pandas DataFrame

This tutorial will discuss about different ways to replace NaN with None in pandas dataframe.

Table Of Contents

Preparing DataSet

First we will create a DataFrame, which has 3 columns, and six rows. This DataFrame has certain NaN values.

import pandas as pd
import numpy as np

# List of Tuples
players = [ ('Suse',   123,     789),
            ('Aadi',   np.NaN,  np.NaN),
            ('Susen',  132,     np.NaN),
            ('Shaunak',789,     np.NaN),
            ('Path',   np.NaN,  666),
            ('Ria',    890,     np.NaN)]

# Create a DataFrame object from list of tuples
df = pd.DataFrame(players,
                  columns=['Name', 'Level_1 Score', 'Level_2 Score'])

print(df)

Output

      Name  Level_1 Score  Level_2 Score
0     Suse          123.0          789.0
1     Aadi            NaN            NaN
2    Susen          132.0            NaN
3  Shaunak          789.0            NaN
4     Path            NaN          666.0
5      Ria          890.0            NaN

Now we want to replace NaN values in all columns of this DataFrame with the value None. Let’s see how to do that.

Replace NaN with None 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 None, create a dictionary containing only one key-value pair. Where key is ‘NaN’, and value is None. Then Pass that dictionary as an argument to the replace() function. It will replace all occurrences of NaN with None in the complete DataFrame. Also, pass inplace as True, due to which all modifications in DataFrame will be in place.

# Replace NaN with None in whole DataFrame
df.replace({np.nan: None}, inplace=True)

print(df)

Output

      Name Level_1 Score Level_2 Score
0     Suse         123.0         789.0
1     Aadi          None          None
2    Susen         132.0          None
3  Shaunak         789.0          None
4     Path          None         666.0
5      Ria         890.0          None

It replaced all the NaN values with None in all the columns of DataFrame.

Summary

We learned thow to replace all occurrences of NaN values with None in complete DataFrame in Pandas.