Categorical data represents groups or labels rather than numbers — city, product category, education level. Most ML algorithms are purely numeric under the hood, so categorical features must be encoded before a model can use them.
Nominal vs Ordinal — The Distinction That Decides Which Encoding to Use
| Type | Meaning | Example | Encoding |
|---|---|---|---|
| Nominal | Categories with no inherent order | City, color, payment method | One-Hot Encoding |
| Ordinal | Categories with a meaningful order | Education level (High School < Bachelor's < Master's), rating (Low/Medium/High) | Ordinal Encoding |
Cardinality Matters
df["city"].nunique() # low cardinality (e.g. 15 cities) -> one-hot encoding is fine
df["product_id"].nunique() # high cardinality (e.g. 50,000 products) -> one-hot would explode column countA "low-cardinality" categorical feature (few unique values) one-hot encodes cleanly. A "high-cardinality" feature (thousands of unique values — user IDs, product SKUs) one-hot encodes into an impractically wide, mostly-zero matrix; frequency encoding, target encoding, or embeddings are usually better fits there.
Practical Use Cases
- Deciding upfront whether a column is nominal or ordinal — gets the encoding choice right the first time
- Checking cardinality before committing to one-hot encoding on a wide categorical column
Common Mistakes
- Label-encoding a nominal feature (like city) with arbitrary integers, which implies a false order the model may pick up on — see Label Encoding.
- One-hot encoding a high-cardinality column without checking
nunique()first, silently creating thousands of sparse columns.
Interview Relevance
Q: "Why shouldn't you always use one-hot encoding for categorical features?" It works well for low-cardinality nominal features, but creates an impractically wide, sparse matrix for high-cardinality features — and it discards ordering information that ordinal encoding would preserve for genuinely ordered categories.
Practice Question
Classify each as nominal or ordinal: (a) blood type, (b) shirt size (S/M/L/XL), (c) country of residence, (d) customer satisfaction rating (1–5).
