Review · Gaps and duplicates

Types, dates & missingness · WR3 · 12 MIN

Review · Gaps and duplicates

Task 1 · Retrieve and combine

Exercises within this concept

Task 1Retrieve and combineCurrent exercise
Task 2Retrieve and combine
Task 3Retrieve and combine
Task 4Retrieve and combine

Task 1 · Messy pet supplies

Create a separate clean copy of df with parsed dates.

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

Your requirements

Keep only records dated on or after 2026-08-04 and sort them by date ascending.
Display clean; preserve df.

Your inputs

Messy pet supplies · 6 synthetic rows
order	item	package	price	discount	date
301	" hay-bale "	BULK	8.50	1	2026-08-01
302	FOOD	small	12	2	2026-08-02
303	" toy-ball "	SINGLE	bad	None	invalid
304	Bowl	Small	None	0.5	2026-08-04
304	Bowl	Small	None	0.5	2026-08-04
305	" food "	BULK	6	1.5	2026-08-06

Revisit the concept lesson →

Remember the idea

Parse date strings before comparing calendar dates. An explicit format tells pandas which part is the year, month and day.

A small example

"2026-08-04" with "%Y-%m-%d" means 4 August 2026. Invalid text becomes NaT with errors="coerce".

What each choice does
Code or choice	Meaning
`%Y / %m / %d`	Four-digit year / month number / day number; literal hyphens match the input separators.
`errors="coerce"`	Make invalid dates missing (NaT); the default raises a parsing error.
`parsed >= "2026-08-04"`	Keep that date and later dates. Missing dates do not pass this comparison.

Hint

Parse before chronological comparison; invalid dates will not pass.

Reveal solution

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

clean = df.copy()
clean["date"] = pd.to_datetime(clean["date"], format="%Y-%m-%d", errors="coerce")
clean = clean[clean["date"] >= "2026-08-04"].sort_values("date")
clean

Your task · Task 1

Create a separate clean copy of df with parsed dates.
Keep only records dated on or after 2026-08-04 and sort them by date ascending.
Display clean; preserve df.

YOUR PYTHON

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

# Supplied setup — editable
import pandas as pd

data = {
    "order": [301, 302, 303, 304, 304, 305],
    "item": [" hay-bale ", "FOOD", " toy-ball ", "Bowl", "Bowl", " food "],
    "package": ["BULK", "small", "SINGLE", "Small", "Small", "BULK"],
    "price": ["8.50", "12", "bad", None, None, "6"],
    "discount": [1, 2, None, 0.5, 0.5, 1.5],
    "date": [
        "2026-08-01",
        "2026-08-02",
        "invalid",
        "2026-08-04",
        "2026-08-04",
        "2026-08-06",
    ],
}

df = pd.DataFrame(data)

# Your work
# Retrieve the earlier skill

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.