Filling Missing Values with the Mean Using Pandas

Last update on December 21 2024 07:41:24 (UTC/GMT +8 hours)

Pandas: Machine Learning Integration Exercise-4 with Solution

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.

Python-Pandas Code Editor:

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