Validating date formats in a Pandas DataFrame

Last update on April 09 2025 12:55:16 (UTC/GMT +8 hours)

9. Validating Date Formats in a DataFrame

Write a Pandas program to validate date formats in a DataFrame.

The following exercise demonstrates how to validate that a column contains correctly formatted date values using to_datetime().

Sample Solution :

Code :

import pandas as pd

# Create a sample DataFrame with date strings
df = pd.DataFrame({
    'Event': ['Conference', 'Meeting', 'Seminar'],
    'Date': ['2020-05-01', '2020-06-15', 'Invalid-Date']
})

# Validate the 'Date' column by converting it to datetime
df['Valid_Date'] = pd.to_datetime(df['Date'], errors='coerce')

# Output the result
print(df)

Output:

        Event          Date Valid_Date
0  Conference    2020-05-01 2020-05-01
1     Meeting    2020-06-15 2020-06-15
2     Seminar  Invalid-Date        NaT

Explanation:

• Created a DataFrame with date strings, including an invalid date.
• Used to_datetime() to validate the 'Date' column, converting invalid dates to NaT.
• Displayed the DataFrame with a new 'Valid_Date' column that contains validated dates.

For more Practice: Solve these Related Problems:

• Write a Pandas program to validate the date format in a specific column and output rows with incorrectly formatted dates.
• Write a Pandas program to convert a column to datetime format and report any parsing errors.
• Write a Pandas program to check that dates in a column conform to a specified format and flag non-conforming entries.
• Write a Pandas program to validate date formats and split a datetime column into separate date and time columns.

Python-Pandas Code Editor:

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