Validating Data Type of a Specific Column in Pandas

Last update on December 21 2024 07:41:44 (UTC/GMT +8 hours)

Pandas: Data Validation Exercise-3 with Solution

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.

Python-Pandas Code Editor:

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