Replacing missing data with column Mean in Pandas

Last update on May 05 2025 13:03:37 (UTC/GMT +8 hours)

12. Dropping Rows with Missing Data

Write a Pandas program to replacing missing data with mean value.

This exercise shows how to replace missing values in a numerical column with the mean of that column.

Sample Solution :

Code :

import pandas as pd

# Create a sample DataFrame with missing values
df = pd.DataFrame({
    'Name': ['Selena', 'Annabel', 'Caeso', 'David'],
    'Age': [25, None, 22, None]
})

# Fill missing 'Age' values with the column's mean
df['Age'].fillna(df['Age'].mean(), inplace=True)

# Output the result
print(df)

Output:

      Name   Age
0   Selena  25.0
1  Annabel  23.5
2    Caeso  22.0
3    David  23.5

Explanation:

Created a DataFrame with missing values in the 'Age' column.
Replaced missing values in 'Age' with the mean of the column using fillna().
Returned the DataFrame with missing data replaced by the mean value.

For more Practice: Solve these Related Problems:

Write a Pandas program to drop rows with any missing values and report the number of rows removed.
Write a Pandas program to drop rows with missing values only in a specified subset of columns.
Write a Pandas program to drop rows with missing values and then reset the DataFrame index.
Write a Pandas program to drop rows with missing values and compare the shape of the DataFrame before and after dropping.

Go to:

Previous: Reordering and Splitting Columns.
Next: Changing Column Names to Lowercase.

Python-Pandas Code Editor:

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