Protect the original

A safe working copy · W01 · 8 MIN

Make changes on a separate working copy.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt the workflow
TransferChoose and combine

Understand the idea

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

Illustration · not the exercise output

A small example

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

Follow the code

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

clean = df.copy()
clean = clean[["candy", "price"]]
clean

What each part does

clean =: name for the working table
df.copy(): make a separate DataFrame

Other choices for later exercises

clean["price"] =: replace a column in the copy

Your inputs

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

Candy shop · 6 synthetic rows
candy	flavour	price	rating	shelf
Gummy Bear	fruity	1.2	4.1	A
Choco Pop	chocolate	2.1	4.6	B
Mint Bite	mint	1.5	3.8	A
Berry Loop	fruity	2.8	4.4	B
Cocoa Cube	chocolate	3.4	4.9	A
Lemon Drop	fruity	1.8	4	B

Your task · Follow

Prepare a separate price-list copy named clean from df.
Keep candy and price in that order; leave the original df intact and display clean.

Hint

Make the copy before preparing the handoff.

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[["candy", "price"]]
clean

Your task · Follow

Prepare a separate price-list copy named clean from df.
Keep candy and price in that order; leave the original df intact and display clean.

YOUR PYTHON

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

# Supplied setup — editable
import pandas as pd

data = {
    "candy": [
        "Gummy Bear",
        "Choco Pop",
        "Mint Bite",
        "Berry Loop",
        "Cocoa Cube",
        "Lemon Drop",
    ],
    "flavour": ["fruity", "chocolate", "mint", "fruity", "chocolate", "fruity"],
    "price": [1.2, 2.1, 1.5, 2.8, 3.4, 1.8],
    "rating": [4.1, 4.6, 3.8, 4.4, 4.9, 4],
    "shelf": ["A", "B", "A", "B", "A", "B"],
}

df = pd.DataFrame(data)

# Your work
clean = df.____()
clean = clean[["candy", "price"]]
clean

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.