Describe numeric columns

Reading the whole table · I18 · 8 MIN

Read a compact numerical profile.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferChoose and combine

Understand the idea

describe gives several summaries of the selected numeric values. Each output column describes one input measurement.

Illustration · not the exercise output

A small example

For [2, 4, 6], count is 3, mean is 4, min is 2, 50% is 4 and max is 6.

Follow the code

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

df[["price", "rating"]].describe()

What each part does

df.describe(): summary rows for numeric columns
50%: median, with half the values at or below it
std: spread around the mean

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 describe() for the numeric columns price and rating, in that order.

Hint

Select the requested numeric columns in the requested order before summarizing.

Reveal solution

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

df[["price", "rating"]].describe()

Your task · Follow

Using df, return describe() for the numeric columns price and rating, in that order.

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.