dt accessor for datetime properties in Pandas - Mini Project: Build & Apply

Exploring Dates with dt Accessor in pandas

📖 Scenario: You work at a small event company. You have a list of event dates and want to learn more about them, like which month and day of the week each event falls on.

🎯 Goal: Build a small pandas program that creates a DataFrame with event dates, extracts the month and day of the week using the dt accessor, and prints the results.

📋 What You'll Learn

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

Create a variable called date_column that holds the event_date column from the DataFrame.

Use the dt accessor on date_column to create two new columns: month and day_of_week.

Print the final DataFrame showing the original dates and the extracted month and day of the week.

💡 Why This Matters

🌍 Real World

Event planners often need to analyze dates to schedule activities, send reminders, or find patterns in attendance.

💼 Career

Data analysts and scientists use datetime properties to prepare and explore time-based data for reports and decision-making.

1

Create the DataFrame with event dates

Create a pandas DataFrame called df with a column event_date containing these exact dates as strings: '2024-01-15', '2024-02-20', '2024-03-10', '2024-04-25', '2024-05-30'. Then convert the event_date column to datetime type using pd.to_datetime().

Pandas

import pandas as pd
# Create DataFrame df with event_date column and convert to datetime
# Your code here

Need a hint?

Use pd.DataFrame to create the table and pd.to_datetime() to convert the date strings.

2

Create a variable for the date column

Create a variable called date_column and assign it the event_date column from the DataFrame df.

Pandas

import pandas as pd

df = pd.DataFrame({
    'event_date': ['2024-01-15', '2024-02-20', '2024-03-10', '2024-04-25', '2024-05-30']
})
df['event_date'] = pd.to_datetime(df['event_date'])
# Create date_column variable for df['event_date']
# Your code here

Need a hint?

Assign df['event_date'] to a new variable called date_column.

3

Extract month and day of week using dt accessor

Use the dt accessor on date_column to create two new columns in df: month with the month number, and day_of_week with the day name.

Pandas

import pandas as pd

df = pd.DataFrame({
    'event_date': ['2024-01-15', '2024-02-20', '2024-03-10', '2024-04-25', '2024-05-30']
})
df['event_date'] = pd.to_datetime(df['event_date'])

date_column = df['event_date']
# Create df['month'] and df['day_of_week'] using date_column.dt
# Your code here

Need a hint?

Use date_column.dt.month for month numbers and date_column.dt.day_name() for day names.

4

Print the final DataFrame

Write a print statement to display the DataFrame df showing the event_date, month, and day_of_week columns.

Pandas

import pandas as pd

df = pd.DataFrame({
    'event_date': ['2024-01-15', '2024-02-20', '2024-03-10', '2024-04-25', '2024-05-30']
})
df['event_date'] = pd.to_datetime(df['event_date'])

date_column = df['event_date']

df['month'] = date_column.dt.month
df['day_of_week'] = date_column.dt.day_name()
# Print the DataFrame df
# Your code here

Need a hint?

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