Recode categories

New columns & clean text · W09 · 8 MIN

Map known labels and preserve unknown ones deliberately.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferCombine earlier skills

Understand the idea

replace and map both use a lookup dictionary, but handle unlisted values differently.

Illustration · not the exercise output

A small example

For ["A", "B"] and {"A": 1}: replace gives [1, "B"]; map gives [1, missing].

Follow the code

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

df["flavour"] = df["flavour"].replace({"fruity": "fruit"})
df

What each part does

replace({"old": "new"}): preserve other values

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

The supplied catalogue df is adopting the label fruit in place of fruity.
Update flavour while retaining all other labels and columns; display df.
Use: replace().

Hint

replace updates the named category without erasing the others.

Reveal solution

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

df["flavour"] = df["flavour"].replace({"fruity": "fruit"})
df

Optional stretch

Try map with the same dictionary. Use isna().sum() to see the difference.

Your task · Follow

The supplied catalogue df is adopting the label fruit in place of fruity.
Update flavour while retaining all other labels and columns; display df.
Use: replace().

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["flavour"] = df["flavour"].____({"fruity": "fruit"})
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.