What columns arrived?

Getting oriented · I04 · 8 MIN

Find the exact column and row labels.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferCombine earlier skills

Understand the idea

Columns name the fields; the index labels the rows. Labels are identifiers and need not match numbered positions.

Illustration · not the exercise output

A small example

With columns name, age and row labels A, B: list(df.columns) is ["name", "age"]; list(df.index) is ["A", "B"].

Follow the code

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

list(df.columns)

What each part does

df.columns: column labels in order
list(...): turn an Index into a familiar Python list

Other choices for later exercises

df.index: row labels in order

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

Using df, return the column labels of Candy shop in their original order as a list.
Use: columns.

Hint

columns describes fields; index describes row labels.

Reveal solution

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

list(df.columns)

Optional stretch

Run list(df.index). After filtering, would you expect those labels to be renumbered automatically?

Your task · Follow

Using df, return the column labels of Candy shop in their original order as a list.
Use: columns.

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
list(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.