Find extremes

Understanding values · I13 · 8 MIN

Ask for the largest or smallest observations.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferChoose and combine

Understand the idea

Extreme-value selection ranks whole records by a numeric column, then returns the requested number.

Illustration · not the exercise output

A small example

For scores [4, 9, 6], nlargest(2, "score") returns the rows for 9 then 6.

Follow the code

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

df.nlargest(2, "price")

What each part does

df.nlargest(2, "price"): two highest-price rows

Other choices for later exercises

df.nsmallest(2, "price"): two lowest-price rows

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 2 rows with the largest price, highest first.
Use: nlargest().

Hint

The count and ranking column are separate arguments; rank whole rows rather than sorting one Series.

Reveal solution

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

df.nlargest(2, "price")

Optional stretch

Find the two smallest instead. Compare with sort_values(...).head(2).

Your task · Follow

Using df, return the 2 rows with the largest price, highest first.
Use: nlargest().

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.