PCA checkpoint · Data Playground

Retrieval 1 · penguins

Fit Penguin PCA, retain at least 90% variance, label weights and report the separate 2D view.

This practice: Fit Penguin PCA, retain at least 90% variance, label weights and report the separate 2D view. Next: Choose and Explain Models. Continue to Choose and Explain Models →

Given data · penguins

333 observations. One measured penguin. The dataframe df is supplied afresh for each Run.

Download source CSV · Source and original dictionary

Reference labels are omitted from this preview and must remain outside fitting.

penguins · first 8 prepared 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

Column meanings and units

Bill length/depth and flipper length: mm. Body mass: grams. Year and island: sampling context.

ML uses 333 complete cases. Removing incomplete records may change the represented population. Geographic context may not generalise to new islands.

Input schema
Column	Stored type
island	str
bill_length_mm	float64
bill_depth_mm	float64
flipper_length_mm	int64
body_mass_g	int64
sex	str
year	int64

Supporting concepts: Two dimensions are a view →

Remember the idea

This checkpoint combines previously taught skills. Assemble the workflow; help remains available when needed.

Schematic · Fit Penguin PCA, retain at least 90% variance, label weights and report the separate 2D view.Scroll the diagram horizontally if needed.

Required Python variables and evidence

Use these names so Check can inspect your workflow. Each meaning is shown beside its name.

Workflow evidence contract
Variable	Meaning
X	Feature dataframe for the declared population, preserving row indices.
cumulative	Cumulative sum of ratios.
pca	PCA fitted on scaled measurements.
ratios	Explained variance ratios in descending component order.
reduced	Scores for the retained component prefix.
retained	Smallest number of components reaching at least 90% variance.
scaled	Standardised features in the same row/column order as X.
scores2	Row-by-two component scores, with signs consistent with weights.
weights	Feature-by-two dataframe of PC1/PC2 axis weights, labelled by feature and component.

Hint 1 — Think

Reconstruct the distinction between retained representation and a convenient picture.

Hint 2 — Tools

Measurement selection, scaling, PCA, cumulative variance and labelled weights/scores.

Hint 3 — Approach

Fit the measurement representation, find the smallest 90% prefix and report it separately from the first-two-axis view.

Explained solution

from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
X = df[['bill_length_mm','bill_depth_mm','flipper_length_mm','body_mass_g']]
scaler = StandardScaler()
scaled = scaler.fit_transform(X)
pca = PCA().fit(scaled)
scores = pca.transform(scaled)
ratios = pca.explained_variance_ratio_
cumulative = np.cumsum(ratios)
retained = int(np.searchsorted(cumulative,0.9)+1)
reduced = scores[:,:retained]
weights = pd.DataFrame(pca.components_[:2].T.copy(),index=X.columns,columns=['PC1','PC2'])
scores2 = scores[:,:2].copy()
fig, ax = plt.subplots(figsize=(6,4))
ax.scatter(scores2[:,0],scores2[:,1],s=12)
ax.set(title='Two-component view of measurements',xlabel='PC1 score',ylabel='PC2 score')
print('Retained dimensions:',retained,'; variance visible in 2D:',ratios[:2].sum())

The retained matrix fulfils the variance rule while the labelled two-dimensional display communicates only the variation its axes contain.

Helpful prior knowledge: PCA retrieval These links are guidance, not locks.

Sources and API context

Examples run with this Playground’s scikit-learn 1.4.2 / Pyodide 0.26.4 runtime.