Inspect numeric relationships

Reading the whole table · I21 · 8 MIN

Inspect numeric relationships

Read correlation as association, not causation.

Exercises within this concept

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferCombine earlier skills

Understand the idea

Correlation summarizes how two numeric measurements move together along a straight-line pattern.

Illustration · not the exercise output

A small example

If x increases as y increases, correlation is positive. If y decreases instead, it is negative.

Follow the code

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

df.corr(numeric_only=True)

What each part does

df.corr(numeric_only=True): correlations among numeric columns
numeric_only=True: leave text columns out
diagonal: a varying column correlates 1 with itself

Your inputs

The editable setup on the right creates df. Run executes the setup and your work from top to bottom.

Study club · 8 synthetic rows
student	club	hours	score	group
Ari	Art	1	52	A
Bo	Code	3	71	A
Cy	Art	2	65	B
Dee	Code	4	82	B
Eli	Art	5	89	A
Flo	Code	3	76	A
Gus	Art	6	93	B
Han	Code	2	61	B

Your task · Follow

Using df, return the correlation matrix for all numeric columns in Study club.

Hint

Select numeric measurements and remember that a matrix reports pairwise linear associations.

Reveal solution

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

df.corr(numeric_only=True)

Your task · Follow

Using df, return the correlation matrix for all numeric columns in Study club.

YOUR PYTHON

Tab: indent · Shift+Tab: outdent · Esc, then Tab: leave editor

# Supplied setup — editable
import pandas as pd

data = {
    "student": ["Ari", "Bo", "Cy", "Dee", "Eli", "Flo", "Gus", "Han"],
    "club": ["Art", "Code", "Art", "Code", "Art", "Code", "Art", "Code"],
    "hours": [1, 3, 2, 4, 5, 3, 6, 2],
    "score": [52, 71, 65, 82, 89, 76, 93, 61],
    "group": ["A", "A", "B", "B", "A", "A", "B", "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.