Align Series when building a table

Understand the idea

A DataFrame built from Series matches their index labels, regardless of displayed order.

Illustrative data

pd.DataFrame({"mean": means, "records": counts})

means
Label	Mean
A	4
B	9

counts
Label	Records
B	3
A	2

→

Aligned result
Label	`mean`	`records`
A	4	2
B	9	3

Follow the code

pd.DataFrame(
    {
        "mean": means,
        "records": counts,
    }
)

What the syntax does

pd.DataFrame({"mean": means, "records": counts}): Match the Series by bench label.
series.index: Labels used to match values.
series.to_numpy(): Remove labels; table construction then matches positions.

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

Your task · Follow

How can summaries in different orders be combined safely?

Use supplied Series means and counts from df.
Display a DataFrame with mean, then records, aligned by bench label.
Keep bench labels as the index.

Setup is included in the editor.

Hint

Pass the Series themselves to the constructor so pandas can match labels.

Reveal solution

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

pd.DataFrame(
    {
        "mean": means,
        "records": counts,
    }
)

# 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) means = df.groupby("bench")["reading"].mean() counts = df["bench"].value_counts().sort_values() # Your work pd.DataFrame( { "mean": means, "records": ____, } )

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