Drop columns

A safe working copy · W04 · 8 MIN

Remove an explicitly unwanted field.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferChoose and combine

Understand the idea

drop removes specified labels. Use columns= to make it clear that you are removing fields rather than rows.

Illustration · not the exercise output

A small example

Dropping note from name, age, note leaves name, age and the same records.

Follow the code

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

df = df.drop(columns=["shelf"])
df

What each part does

drop(columns=["shelf"]): remove the named column
columns=: make the direction explicit

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

Remove the shelf column from df and preserve every row.
Keep the changes in df and display it.
Use: drop().

Hint

Use columns= so drop does not interpret the names as row labels.

Reveal solution

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

df = df.drop(columns=["shelf"])
df

Your task · Follow

Remove the shelf column from df and preserve every row.
Keep the changes in df and display it.
Use: drop().

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
df = ____
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.