Sampled hierarchies describe sampled rows

Understand the idea

Production fits the scale on the exploratory population, then transforms a reproducible sample of at most 500 rows for the hierarchy. The resulting labels belong to those sampled rows.

Illustrative data

Click the highlighted code to change the result.

sample = example.sample(, random_state=42)

Before
original row	length_mm	width_cm
w1	10	1
w2	11	1.3
w3	12	0.7
w4	25	6
w5	26	6.2
w6	40	2
w7	41	2.4
w8	42	1.7

Sample
original row	length_mm	width_cm
w2	11	1.3
w6	40	2
w1	10	1
w8	42	1.7

Sample 4 rows with seed 42, preserving their values and original indices.

What the syntax does

X.sample: Selects rows while preserving their original index labels.

Supplied for this practice

These objects are prepared afresh before your editor runs.

X: Feature dataframe for the declared population, preserving row indices.
X = df[['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g']]

Outputs you create

answer: The requested result, with the values, labels and row order described by this task.

penguins · first 8 rows
`island`	`bill_length_mm`	`bill_depth_mm`	`flipper_length_mm`	`body_mass_g`	`sex`	`year`
Torgersen	39.1	18.7	181	3750	male	2007
Torgersen	39.5	17.4	186	3800	female	2007
Torgersen	40.3	18	195	3250	female	2007
Torgersen	36.7	19.3	193	3450	female	2007
Torgersen	39.3	20.6	190	3650	male	2007
Torgersen	38.9	17.8	181	3625	female	2007
Torgersen	39.2	19.6	195	4675	male	2007
Torgersen	41.1	17.6	182	3200	female	2007

Your task

Sample 100 Penguin measurement rows with seed 42.
Store the original values and indices in answer.

answer

Hints

Think: Original indices preserve the link between sampled observations and their measurements.

Tools: DataFrame.sample with random_state.

Approach: Take the requested reproducible sample without resetting its index.

Explained solution

answer=X.sample(100,random_state=42)

Retaining row identity lets later assignments and original-unit profiles remain aligned to the sampled population.