Filling missing data in a DataFrame using fillna()

Last update on April 09 2025 12:54:18 (UTC/GMT +8 hours)

1. Handling Missing Data in Pandas

Write a Pandas program to fill missing values (NaN) in a DataFrame using fillna().

In this exercise, you will learn how to fill missing values (NaN) in a DataFrame using fillna() by replacing them with a constant value.

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]
})
# Fill missing values with a constant
df_filled = df.fillna(value={'Name': 'Unknown', 'Age': 0, 'Salary': 0})
# Output the result
print(df_filled)

Output:

      Name   Age   Salary
0    David  25.0  50000.0
1  Annabel  30.0      0.0
2  Charlie   0.0  70000.0
3  Unknown  22.0  60000.0

Explanation:

• Created a DataFrame with some missing values (None).
• Used fillna() to fill missing values with constant replacements: 'Unknown' for names, and 0 for missing ages and salaries.
• Returned the DataFrame with missing values replaced.

For more Practice: Solve these Related Problems:

• Write a Pandas program to fill missing values in a DataFrame using the previous row value.
• Write a Pandas program to replace NaN values with the most frequent value in each column.
• Write a Pandas program to interpolate missing values using different methods (linear, polynomial, etc.).
• Write a Pandas program to count the number of missing values in each column.

Python-Pandas Code Editor:

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