Work with dates

Types, dates & missingness · W15 · 8 MIN

Extract useful calendar features from parsed dates.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferChoose and combine

Understand the idea

The .dt accessor extracts calendar information from a datetime Series. Parse text first; missing dates yield missing calendar values.

Illustration · not the exercise output

A small example

For 2026-08-04: year is 2026, month is 8 and day_name() is "Tuesday".

Follow the code

Apply the idea to the supplied table. Read from top to bottom; the final line displays the result.

df["date"] = pd.to_datetime(df["date"], format="%Y-%m-%d", errors="coerce")
df["year"] = df["date"].dt.year
df["month"] = df["date"].dt.month
df["weekday"] = df["date"].dt.day_name()
df

What each part does

df["date"].dt.year: calendar year
.dt.month: month number
.dt.day_name(): weekday name

Your inputs

The editable setup on the right creates df. Run executes the setup and your work from top to bottom.

Messy café orders · 8 synthetic rows
order	drink	size	price	tip	date
101	" latte "	LARGE	6.20	1	2026-06-01
102	TEA	small	3.10	0.5	2026-06-02
103	" mocha "	LARGE	oops	None	not a date
104	Latte	Small	None	0.8	2026-06-04
104	Latte	Small	None	0.8	2026-06-04
105	"tea "	SMALL	4.20	0.6	2026-06-05
106	ESPRESSO	small	2.50	0.2	2026-06-06
107	" mocha"	large	6.80	1.5	2026-06-07

Your task · Follow

Using df, parse date, then add year, month and weekday columns.
Retain invalid dates as missing.
Keep the changes in df and display it.
Use: to_datetime().

Hint

Parse dates before using .dt; year and month have no parentheses, but day_name does.

Reveal solution

One way to do it. Keep any supplied setup in the editor and use this in the Your work section.

df["date"] = pd.to_datetime(df["date"], format="%Y-%m-%d", errors="coerce")
df["year"] = df["date"].dt.year
df["month"] = df["date"].dt.month
df["weekday"] = df["date"].dt.day_name()
df

Your task · Follow

Using df, parse date, then add year, month and weekday columns.
Retain invalid dates as missing.
Keep the changes in df and display it.
Use: to_datetime().

YOUR PYTHON

Tab: indent · Shift+Tab: outdent · Esc, then Tab: leave editor

# Supplied setup — editable
import pandas as pd

data = {
    "order": [101, 102, 103, 104, 104, 105, 106, 107],
    "drink": [
        " latte ",
        "TEA",
        " mocha ",
        "Latte",
        "Latte",
        "tea ",
        "ESPRESSO",
        " mocha",
    ],
    "size": ["LARGE", "small", "LARGE", "Small", "Small", "SMALL", "small", "large"],
    "price": ["6.20", "3.10", "oops", None, None, "4.20", "2.50", "6.80"],
    "tip": [1, 0.5, None, 0.8, 0.8, 0.6, 0.2, 1.5],
    "date": [
        "2026-06-01",
        "2026-06-02",
        "not a date",
        "2026-06-04",
        "2026-06-04",
        "2026-06-05",
        "2026-06-06",
        "2026-06-07",
    ],
}

df = pd.DataFrame(data)

# Your work
df["date"] = ____
df["year"] = ____
df["month"] = ____
df["weekday"] = ____
df

Each run executes all editor code in a fresh Python session. Display a value by leaving it on the final line.

Python starts when you open a lesson.

Output

Run your code to see what Python returns.