Ordinal encoding changes how raw variables are represented for analysis or modelling. The transformation should preserve the information needed by the task while making assumptions explicit and reproducible.
Ordinal encoding changes how raw variables are represented for analysis or modelling. The transformation should preserve the information needed by the task while making assumptions explicit and reproducible.
The practical value of Ordinal encoding comes from understanding both the transformation and the boundary around it: what information is allowed to enter, what assumption is being made, and how you know the result is still valid after the transformation.
A beginner-friendly way to reason about it is to start with a tiny case where the correct result can be checked independently. Once the mechanism is clear, scale the exact same reasoning to larger tables, pipelines or models.
Fit transformation parameters on training data, apply exactly the learned transformation to validation/test/new data, and preserve the fitted transformer with the model.
The interactive view uses a concept-specific plot when the topic maps naturally to one; otherwise it uses a workflow view instead of leaving a broken placeholder.
Standardise continuous variables for a distance-based model while one-hot encoding nominal categories in the same pipeline.
A useful diagnostic question is: Could the same code still run successfully if the analytical assumption were wrong? If yes, add an explicit validation check rather than relying on execution success.
Keep the example small enough that you can inspect each stage manually.
# Step 1 — Import the module so its functions/classes are available to the rest of this example.
import numpy as np
# Step 2 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.preprocessing import OrdinalEncoder
# Step 3 — Construct `X` as an array so vectorised numerical operations can be applied consistently.
X = np.array([["low"],["medium"],["high"],["medium"]])
# Step 4 — Fit the model or transformer, learning its parameters from the supplied training data.
enc = OrdinalEncoder(categories=[["low","medium","high"]]).fit(X)
# Step 5 — Apply the already-fitted transformation without relearning its parameters from this data.
Z = enc.transform(X)
# Step 6 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 1 · categories:", enc.categories_[0].tolist())
# Step 7 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 2 · encoded shape:", Z.shape)
# Step 8 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 3 · values:", Z.ravel().astype(int).tolist())STEP 1 · categories: ['low', 'medium', 'high'] STEP 2 · encoded shape: (4, 1) STEP 3 · values: [0, 1, 2, 1]