Pandas - Applying a custom function to extract year from dates in DataFrame

Last update on April 09 2025 12:53:09 (UTC/GMT +8 hours)

13. Transform Dates with apply()

Write a Pandas program that applies a function to transform dates in a DataFrame.

This exercise will show how to apply a custom function to transform date values in a DataFrame.

Sample Solution :

Code :

import pandas as pd

# Create a sample DataFrame with date strings
df = pd.DataFrame({
    'Date': ['2023-01-01', '2023-06-15', '2023-12-31']
})

# Convert the 'Date' column to datetime
df['Date'] = pd.to_datetime(df['Date'])

# Define a custom function to extract the year
def get_year(date):
    return date.year

# Apply the function to extract the year from each date
df['Year'] = df['Date'].apply(get_year)

# Output the result
print(df)

Output:

        Date  Year
0 2023-01-01  2023
1 2023-06-15  2023
2 2023-12-31  2023

Explanation:

Created a DataFrame with a 'Date' column containing string dates.
Converted the 'Date' column to a datetime format using pd.to_datetime().
Defined a function get_year() to extract the year from a date.
Applied this function to extract the year from each date in the 'Date' column.
Added the extracted year as a new column 'Year' in the DataFrame.

For more Practice: Solve these Related Problems:

Write a Pandas program to apply a custom function to convert date strings to datetime objects in a DataFrame column using apply().
Write a Pandas program to use apply() to extract the month and day from a date column and create separate columns.
Write a Pandas program to transform a DataFrame’s date column into a different format using a custom function with apply().
Write a Pandas program to apply a function to a date column to compute the day of the week and add it as a new column.

Python-Pandas Code Editor:

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