Filling Missing Values with the Mean Using Pandas

Last update on April 11 2025 12:53:59 (UTC/GMT +8 hours)

4. Filling Missing Values with the Mean

Write a Pandas program that fills missing values with the Mean.

This exercise demonstrates how to fill missing values in numerical columns with the mean of the column.

Sample Solution :

Code :

import pandas as pd

# Load the dataset
df = pd.read_csv('data.csv')

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

# Output the updated dataset
print(df)

Output:

   ID      Name   Age  Gender   Salary  Target
0   1      Sara  25.0  Female  50000.0       0
1   2    Ophrah  30.0    Male  60000.0       1
2   3    Torben  22.0    Male  70000.0       0
3   4  Masaharu  35.0    Male  80000.0       1
4   5      Kaya  28.2  Female  55000.0       0
5   6   Abaddon  29.0    Male      NaN       1

Explanation:

• Loaded the dataset using Pandas.
• Used fillna() to replace missing values in the 'Age' column with the mean of the column.
• Displayed the updated dataset.

For more Practice: Solve these Related Problems:

• Write a Pandas program to fill missing values in a numeric column with the mean and validate the change.
• Write a Pandas program to compute the column-wise mean and fill missing values in multiple columns simultaneously.
• Write a Pandas program to fill missing values with the mean only for rows that meet a specific condition.
• Write a Pandas program to fill missing values with the mean and then plot the distribution before and after imputation.

Python-Pandas Code Editor:

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