Bridge review · Sections 02 + 03

Types, dates & missingness · WR2 · 12 MIN

Bridge review · Sections 02 + 03

Task 1 · Section 02 · W07: create a numeric column

Exercises within this concept

Task 1Section 02 · W07: create a numeric columnCurrent exercise
Task 2Section 02 · W10: clean text
Task 3Section 03 · W12: convert numeric text

Task 1 · Pet adoption

Add age_months to df from age in years (12 months per year).

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

Your requirements

Keep the original age values and all other columns; display df.

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

Arithmetic on columns works row by row. Assigning to a new column name stores a derived measurement alongside the originals.

A small example

Prices [3, 5] plus tips [1, 2] give totals [4, 7]. Ages [2, 3] years become [24, 36] months.

What each choice does
Code or choice	Meaning
`df["new"] = column * number`	Apply the same multiplier to every value.
`df["new"] = first + second`	Combine matching row values; use compatible units.

Hint

A unit conversion belongs in a new, clearly named column.

Reveal solution

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

df["age_months"] = df["age"] * 12
df

Your task · Task 1

Add age_months to df from age in years (12 months per year).
Keep the original age values and all other columns; display df.

YOUR PYTHON

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

# 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

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.