Pandas - Dropping rows with missing data in a DataFrame using dropna()

Last update on April 09 2025 12:54:44 (UTC/GMT +8 hours)

2. Handling Duplicates in Pandas

Write a Pandas program to drop rows with missing data.

This exercise demonstrates how to drop rows that contain missing values using the dropna() function.

Sample Solution :

Code :

import pandas as pd

# Create a sample DataFrame with missing values
df = pd.DataFrame({
    'Name': ['David', 'Annabel', 'Charlie', None],
    'Age': [25, 30, None, 22],
    'Salary': [50000, None, 70000, 60000]
})

# Drop rows that have any missing values
df_dropped = df.dropna()

# Output the result
print(df_dropped)

Output:

    Name   Age   Salary
0  David  25.0  50000.0

Explanation:

• Created a DataFrame with missing values.
• Used dropna() to remove rows that have any missing values.
• Displayed the DataFrame with the rows containing missing values dropped.

For more Practice: Solve these Related Problems:

• Write a Pandas program to remove duplicate rows but keep the first occurrence.
• Write a Pandas program to find and count duplicate values in a specific column.
• Write a Pandas program to drop duplicates based on multiple columns.
• Write a Pandas program to mark duplicate rows with a new column.

Python-Pandas Code Editor:

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