Understand the idea
In supervised learning we had y. Here the goal is to describe structure in X. Cluster IDs are arbitrary identifiers, not predicted real-world classes. Reference labels must not guide fitting or selection.
What the syntax does
df[measurement_columns]- Creates
Xwithout a supervised target or hidden reference label.
- Creates
Outputs you create
answer- The requested result, with the values, labels and row order described by this task.
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
- Store only bill length, bill depth, flipper length and body mass in
answer, keeping every Penguin row and the stated column order.
answerHints
Think: Reference labels and context fields would change the meaning of measurement-based discovery.
Tools: Explicit measurement-column selection.
Approach: Keep only the four declared numeric measurements and inspect the resulting schema.
Explained solution
answer=df[['bill_length_mm','bill_depth_mm','flipper_length_mm','body_mass_g']]
Excluding species and context keeps grouping based on the intended measured similarities rather than known labels.