Order a summary and extract its labels · Data Playground

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Reading the whole table · Lesson 24 · 9 MIN

Order a summary and extract its labels

Use a grouped Series to choose a meaningful category order.

FollowFollow the techniqueCurrent exercise
ChangeAdapt a requirement
TransferChoose and combine

Understand the idea

A grouped Series holds categories in its index and their means in its values.

Illustrative data

Click the highlighted code to change the result.

means = df.groupby()["reading"].mean()
list(means.index)

Source `df`
row	`bench`	`station`	`reading`
0	A	North	2
1	A	South	4
2	B	North	8
3	C	South	6

→

Computed `means` Series
`bench`	`reading`
A	3
B	8
C	6

Result["A", "B", "C"]

Grouping creates one mean per label. means.index returns those group labels, rather than the mean measurements.

Follow the code

medians = df.groupby("bench")["reading"].median()
ranked = medians.sort_values()
order = ranked.index
list(order)

What the syntax does

means = df.groupby("bench")["reading"].mean(): Calculate one mean per bench; bench names become the Series index.
list(means.index): Return those bench labels in their existing order.

Bench summary checks
`bench`	`reading`
Ash	9
Cedar	6
Birch	2
Ash	10
Cedar	8
Birch	4
Birch	60
Ash	11

Your task · Follow

Which category labels belong to the grouped `means`?

Group all df rows by bench and calculate mean reading.
Display the grouped category labels as a list in default label order.
Use the grouped Series index.

Setup is included in the editor.

Hint

Group names become the summary index; the means are different values.

Reveal solution

Keep the supplied setup and replace the Your work section with this example.

means = df.groupby("bench")["reading"].mean()
list(means.index)

YOUR PYTHON

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

# Supplied setup — editable
import pandas as pd

data = {
    "bench": ["Ash", "Cedar", "Birch", "Ash", "Cedar", "Birch", "Birch", "Ash"],
    "reading": [9, 6, 2, 10, 8, 4, 60, 11],
}

df = pd.DataFrame(data)

# Your work
means = df.groupby("bench")["reading"].mean()
list(means.____)

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Output

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