Write a program in Python to split the date column into day, month, year in multiple columns of a given dataframe

When working with date data in a pandas DataFrame, you often need to split a date column into separate day, month, and year columns. This is useful for analysis, filtering, or creating date-based features.

Problem Statement

Given a DataFrame with a date column in "DD/MM/YYYY" format, we want to extract day, month, and year into separate columns ?

     date day month year
0 17/05/2002  17    05 2002
1 16/02/1990  16    02 1990
2 25/09/1980  25    09 1980
3 11/05/2000  11    05 2000
4 17/09/1986  17    09 1986

Using str.split() Method

The most straightforward approach is using pandas str.split() method with the delimiter "/" and expand=True parameter ?

import pandas as pd

# Create a DataFrame with date column
df = pd.DataFrame({
    'date': ['17/05/2002', '16/02/1990', '25/09/1980', '11/05/2000', '17/09/1986']
})

print("Original DataFrame:")
print(df)

# Split date column into day, month, year
df[["day", "month", "year"]] = df["date"].str.split("/", expand=True)

print("\nDataFrame after splitting:")
print(df)

Original DataFrame:
        date
0 17/05/2002
1 16/02/1990
2 25/09/1980
3 11/05/2000
4 17/09/1986

DataFrame after splitting:
        date day month year
0 17/05/2002  17    05 2002
1 16/02/1990  16    02 1990
2 25/09/1980  25    09 1980
3 11/05/2000  11    05 2000
4 17/09/1986  17    09 1986

How It Works

The str.split() method works as follows:

• Delimiter: "/" specifies the character to split on
• expand=True: Returns a DataFrame instead of a Series of lists
• Multiple assignment: Assigns the three resulting columns directly to day, month, year

Alternative: Converting to Datetime

For more robust date handling, you can convert to datetime first ?

import pandas as pd

df = pd.DataFrame({
    'date': ['17/05/2002', '16/02/1990', '25/09/1980', '11/05/2000', '17/09/1986']
})

# Convert to datetime first
df['date_dt'] = pd.to_datetime(df['date'], format='%d/%m/%Y')

# Extract components
df['day'] = df['date_dt'].dt.day
df['month'] = df['date_dt'].dt.month  
df['year'] = df['date_dt'].dt.year

print(df[['date', 'day', 'month', 'year']])

        date  day  month  year
0 17/05/2002   17      5  2002
1 16/02/1990   16      2  1990
2 25/09/1980   25      9  1980
3 11/05/2000   11      5  2000
4 17/09/1986   17      9  1986

Comparison

Conclusion

Use str.split() for simple string-based splitting that preserves formatting. Use to_datetime() when you need proper date validation and further datetime operations.