Validating Data Type of a Specific Column in Pandas

Last update on April 09 2025 12:55:13 (UTC/GMT +8 hours)

3. Validating the Data Type of a Specific Column

Write a Pandas program to validate the data type of a specific column in a DataFrame.

This exercise demonstrates how to validate the data type of a specific column using astype().

Sample Solution :

Code :

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'ID': [1, 2, 3, 4],
    'Price': ['10.5', '20.0', '30.5', '40.0']
})

# Check if the 'Price' column can be converted to float
try:
    df['Price'] = df['Price'].astype(float)
    print("Data type conversion successful.")
except ValueError:
    print("Data type conversion failed.")

Output:

Data type conversion successful

Explanation:

• Created a DataFrame where the 'Price' column is in string format.
• Attempted to convert the 'Price' column to float using astype().
• Used try-except to handle any data type conversion errors, outputting success or failure messages.

For more Practice: Solve these Related Problems:

• Write a Pandas program to validate that a specific column contains only numeric values and report any violations.
• Write a Pandas program to check the data type of a specific column and convert it if it does not match the expected type.
• Write a Pandas program to validate a specific column's data type and output the rows that fail the validation.
• Write a Pandas program to enforce a custom data type for a specific column and generate warnings for non-compliant entries.

Python-Pandas Code Editor:

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