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In this post, you will learn how to replace NaN by mean in Pandas.

There are many methods to get rid of unspecified values from the dataframe. I will use the sklearn module to replace the NaN value with the average.

import pandas as pd

my_data = {'Column1': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7],
           'Column2': [100000000, 120000000, None, 260000000, 210000000, 80000000, 40000000]}

my_df = pd.DataFrame(my_data)

How to replace nan by mean in Pandas

The SimpleImputer method allows you to replace NaN values using various strategies. Below I paste the code that replaces NaN with the average.

import pandas as pd
from sklearn.impute import SimpleImputer


my_data = {'Column1': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7],
           'Column2': [100000000, 120000000, None, 260000000, 210000000, 80000000, 40000000]}

my_df = pd.DataFrame(my_data)

nan_to_mean = SimpleImputer(strategy='mean')

my_df['Column2'] = nan_to_mean.fit_transform(my_df[['Column2']])

print(f'No more NaN values in my dataframe: \n {my_df}')

There are also other strategies for replacing unspecified values. Can replace NaN with:

• mean
• median
• most_frequent
• constant

You can choose the strategy that best suits your data and analysis needs.

See also:
Documentation of SimpleImputer method