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241. Ordinal encoding
easy
Generalsenior
Ordinal encoding is a simple way to turn ordered categorical values (like sizes) into numbers so models can use them. You’ll implement an ordinal encoder that maps each category to an integer based on a provided ordering.
Requirements
Implement the function
python
Rules:
- Use the mapping ( \text{code}(c) = \text{index}(c \text{ in ordered_categories}) ).
- If a value is not present in
ordered_categories, encode it asunknown_value. - Return a NumPy array of integers.
- Do not use any prebuilt encoders (e.g., from scikit-learn).
- Keep it in a single function.
Example
python
Output:
python
Input Signature
| Argument | Type |
|---|---|
| values | np.ndarray |
| unknown_value | int |
| ordered_categories | np.ndarray |
Output Signature
| Return Name | Type |
|---|---|
| value | np.ndarray |
Constraints
Return NumPy array
No scikit-learn or prebuilt encoders
Single function implementation only
Hint 1
Start by thinking: for each value, you need its position in ordered_categories (or a fallback).
Hint 2
Precompute a lookup dict {category: index} using enumerate(ordered_categories) to avoid repeated .index() calls.
Hint 3
Use dict.get(value, unknown_value) inside a list comprehension to produce the final list[int] in one pass.
Roles
ML Engineer
AI Engineer
Data Scientist
Quantitative Analyst
Companies
GeneralLevels
senior
entry
Tags
ordinal-encoding
hash-map
categorical-features
python-list-comprehension
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Python EditorLn 1, Col 1