Extracting year, month, day in Pandas - Mini Project: Build & Apply

Extracting year, month, day from dates using pandas

📖 Scenario: You work in a company that tracks employee joining dates. You have a list of joining dates and want to analyze the year, month, and day separately to find trends.

🎯 Goal: Build a pandas DataFrame with joining dates, then extract the year, month, and day into new columns.

📋 What You'll Learn

Create a pandas DataFrame with a column named joining_date containing specific dates.

Create a variable called date_column that holds the string 'joining_date'.

Use pandas datetime accessor to extract year, month, and day from the joining_date column into new columns named year, month, and day.

Print the final DataFrame to show the extracted columns.

💡 Why This Matters

🌍 Real World

Extracting year, month, and day from dates helps businesses analyze trends over time, like employee joining patterns or sales by month.

💼 Career

Data analysts and data scientists often need to break down dates to perform time-based analysis and reporting.

1

Create the DataFrame with joining dates

Import pandas as pd and create a DataFrame called df with a column joining_date containing these exact dates as strings: '2023-01-15', '2022-12-05', '2021-07-23', '2020-03-30'.

Pandas

import pandas as pd
# Create DataFrame df with joining_date column
# Your code here

Need a hint?

Use pd.DataFrame and pass a dictionary with key 'joining_date' and the list of date strings as value.

2

Create a variable for the date column name

Create a variable called date_column and set it to the string 'joining_date'.

Pandas

import pandas as pd
df = pd.DataFrame({
    'joining_date': ['2023-01-15', '2022-12-05', '2021-07-23', '2020-03-30']
})
# Create variable date_column with value 'joining_date'
# Your code here

Need a hint?

Just assign the string 'joining_date' to the variable date_column.

3

Extract year, month, and day into new columns

Convert the joining_date column to datetime using pd.to_datetime. Then create three new columns in df: year, month, and day by extracting these parts from the datetime column using the pandas datetime accessor .dt and the variable date_column.

Pandas

import pandas as pd
df = pd.DataFrame({
    'joining_date': ['2023-01-15', '2022-12-05', '2021-07-23', '2020-03-30']
})
date_column = 'joining_date'
# Convert joining_date to datetime and extract year, month, day
# Your code here

Need a hint?

Use pd.to_datetime to convert the column, then use .dt.year, .dt.month, and .dt.day to get parts.

4

Print the DataFrame with extracted columns

Print the DataFrame df to display the joining_date, year, month, and day columns.

Pandas

import pandas as pd
df = pd.DataFrame({
    'joining_date': ['2023-01-15', '2022-12-05', '2021-07-23', '2020-03-30']
})
date_column = 'joining_date'
df[date_column] = pd.to_datetime(df[date_column])
df['year'] = df[date_column].dt.year
df['month'] = df[date_column].dt.month
df['day'] = df[date_column].dt.day
# Print the DataFrame df
# Your code here

Need a hint?

Use print(df) to show the DataFrame with the new columns.