Replacing missing data with column Mean in Pandas

Last update on April 09 2025 12:54:40 (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.

Python-Pandas Code Editor:

Have another way to solve this solution? Contribute your code (and comments) through Disqus.