Count categories

Understanding values · I15 · 8 MIN

Compare category frequencies and proportions.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferChoose and combine

Understand the idea

value_counts counts rows for each distinct value. normalize=True divides each count by the number of non-missing values being counted.

Illustration · not the exercise output

A small example

For A, A, B: counts are A: 2 and B: 1; proportions are 2/3 and 1/3; percentages are about 66.7 and 33.3.

Follow the code

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

df["flavour"].value_counts(normalize=True)

What each part does

value_counts(): counts per category
normalize=True: fractions rather than counts

Other choices for later exercises

dropna=False: also count missing 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

Using df, return the proportion of observations in each flavour category.
Use: value_counts().

Hint

normalize=True changes counts to proportions; missing values are excluded by default.

Reveal solution

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

df["flavour"].value_counts(normalize=True)

Your task · Follow

Using df, return the proportion of observations in each flavour category.
Use: value_counts().

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
# Build the requested result from the given table
result = ____
result

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.