Fit a reusable PCA representation

Understand the idea

Standardisation changes which variation PCA emphasises. Fit the representation on the intended population; transform new rows using those same fitted statistics and axes.

Transform rows with the fitted axes and scale.Scroll the diagram horizontally if needed.

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.copy()
scaler: Already fitted coordinate system learned from X; transform reuses its statistics without fitting again. Fitted on X; reuse without refitting.
scaled: Standardised features in the same row/column order as X.
scaled = scaler.fit_transform(X)
pca: Already fitted on scaled. PCA fitted on scaled measurements. Fitted on scaled; reuse without refitting.
scores: Supplied scores; its definition below specifies the values and role.
scores = pca.transform(scaled)

Outputs you create

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

PCA48 · first 8 rows
`a`	`b`	`c`	`d`	`e`
103.047	50.8622	22.7157	44.6184	0.906993
89.6002	44.3015	20.2703	40.1253	-0.680116
107.505	53.9521	21.1565	41.7009	0.818161
109.406	54.2501	22.5252	44.9095	1.39291
80.4896	40.0557	14.1714	29.4087	-3.27953
86.9782	44.1387	18.7213	37.5997	-1.70077
101.278	50.4611	18.1185	36.0784	-0.343557
96.8376	48.7875	17.4445	33.8533	-0.987809

Your task

Store the supplied PCA component scores for every PCA48 row in answer.

answer

Hints

Think: Scores are each observation’s coordinates along the fitted component axes.

Tools: The supplied PCA scores array.

Approach: Return the prepared component-score matrix and inspect its row/column shape.

Explained solution

answer=scores

Rows remain observations while columns become component coordinates rather than original measurements.

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