Review · Keep the evidence · Data Playground

Task 1 · Pet adoption

Prepare a separate clean table for species="Cat", ordered by age largest first.

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

Your requirements

Preserve df and display clean.

Your inputs

Pet adoption · 6 synthetic rows
name	species	age	weight	room
Milo	Cat	3	4.2	A
Pepper	Dog	7	18.5	B
Luna	Cat	2	3.6	A
Bean	Rabbit	4	2.4	B
Rex	Dog	5	22	A
Nori	Rabbit	1	1.8	B

Revisit the concept lesson →

Remember the idea

Make a separate working table before editing. Assigning another name to df alone still refers to the same object.

A small example

clean = df.copy() lets you select or sort clean while keeping the original df available.

What each choice does
Code or choice	Meaning
`clean = df`	Another name for the same table; direct edits can affect df.
`clean = df.copy()`	Independent working DataFrame for these scalar-valued tables.
`clean = clean[...]`	Keep a selection in the working copy; display clean at the end.

Hint

Combine copying, filtering and sorting without assigning back to df.

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 = clean[clean["species"] == "Cat"].sort_values("age", ascending=False)
clean

# Supplied setup — editable import pandas as pd data = { "name": ["Milo", "Pepper", "Luna", "Bean", "Rex", "Nori"], "species": ["Cat", "Dog", "Cat", "Rabbit", "Dog", "Rabbit"], "age": [3, 7, 2, 4, 5, 1], "weight": [4.2, 18.5, 3.6, 2.4, 22, 1.8], "room": ["A", "B", "A", "B", "A", "B"], } df = pd.DataFrame(data) # Your work # Retrieve the earlier skill

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