Removing columns with too many missing values using dropna() in Pandas

Last update on April 09 2025 12:54:39 (UTC/GMT +8 hours)

13. Changing Column Names to Lowercase

Write a Pandas program to remove columns with too many missing values.

Following exercise removes columns that contain too many missing values using dropna().

Sample Solution :

Code :

import pandas as pd

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

# Remove columns with more than 50% missing values
df_cleaned = df.dropna(thresh=2, axis=1)

# Output the result
print(df_cleaned)

Output:

      Name   Age
0   Selena  25.0
1  Annabel   NaN
2    Caeso  22.0

Explanation:

• Created a DataFrame with multiple columns containing missing values.
• Used dropna(thresh=2, axis=1) to remove columns with more than 50% missing values.
• Returned the DataFrame with only columns that have sufficient data.

For more Practice: Solve these Related Problems:

• Write a Pandas program to convert all column names of a DataFrame to lowercase.
• Write a Pandas program to convert all column names to lowercase and remove any special characters.
• Write a Pandas program to convert all column names to lowercase and replace spaces with underscores.
• Write a Pandas program to change column names to lowercase and then sort the columns alphabetically.

Python-Pandas Code Editor:

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