What values exist?

Understanding values · I14 · 8 MIN

Find distinct category values.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferCombine earlier skills

Understand the idea

Distinct values answer “which labels?”; a distinct count answers “how many different labels?”. Neither counts how often each label occurs.

Illustration · not the exercise output

A small example

For ["A", "B", "A"], unique() gives A, B; nunique() gives 2.

Follow the code

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

df["flavour"].nunique()

What each part does

df["flavour"]: Choose the column whose category values you want to count.
.nunique(): Count its distinct known 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 number of distinct non-missing values in flavour.
Use: nunique().

Hint

nunique counts labels; unique returns the labels themselves.

Reveal solution

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

df["flavour"].nunique()

Optional stretch

Run unique() too. How many items are in the result?

Your task · Follow

Using df, return the number of distinct non-missing values in flavour.
Use: nunique().

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.