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Working With Date Values

Once a column is a datetime, you can ask time-based questions.

What hour do orders spike?
Which quarter had the most signups?
Which weekday ~~is cursed~~ has fewer sales?

Pandas exposes date helpers through the .dt accessor, so you can pull out a Series of date parts directly:

df["signup_year"] = df["signup_at"].dt.year  # 2024
df["signup_month"] = df["signup_at"].dt.month  # 1 = January
df["signup_day"] = df["signup_at"].dt.day  # 15
df["signup_hour"] = df["signup_at"].dt.hour  # 0-23
df["signup_weekday"] = df["signup_at"].dt.dayofweek  # 0 = Monday
df["signup_quarter"] = df["signup_at"].dt.quarter  # 2 (April-June)
df["signup_day_name"] = df["signup_at"].dt.day_name()  # "Monday"

Assignment

SnackStack's analytics team is tired of eyeballing raw timestamps in their activity reports. They want a short, human-friendly label on every device reading, like "Monday, Q1 2024".

Complete the label_events function. It accepts a DataFrame with a datetime timestamp column and returns a copy with a single new event_label column – a string that stitches a few date parts together into your own custom format.

For each row, build the label as "<weekday>, Q<quarter> <year>". For example, a timestamp of 2024-01-15 10:30:00 (a Monday) becomes "Monday, Q1 2024".

Create a copy of the DataFrame to avoid modifying the original.
Pull out the weekday name, the quarter, and the year.
The quarter and year come out as numbers, so convert them to strings so you can join them into text.
Combine the parts into a new event_label column formatted as "<weekday>, Q<quarter> <year>". For example, "Monday, Q1 2024".
Return the updated DataFrame.