AI & MACHINE LEARNING PROGRAM • LEVEL 05 — DATA PREPARATION
One-hot Encode Categories with Python
Learn one-hot encode categories with python with a short, executable Python example.
PROBLEM UNDERSTANDING
Input and expected output
No input required
['blue', 'red'] [[0, 1], [1, 0], [0, 1]]
COMPLETE PYTHON PROGRAM
Complete Python implementation
categories = ['red', 'blue', 'red'] labels = sorted(set(categories)) encoded = [[int(value == label) for label in labels] for value in categories] print(labels, encoded)
CURRENT STEP
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SELECTED LINE
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EXPECTED OUTPUT FOR THE SAMPLE
['blue', 'red'] [[0, 1], [1, 0], [0, 1]]
PROGRAM EXPLANATION
Algorithm and explanation
- Initialize the sample values used to one-hot encode categories.
- Process the data step by step using One-hot encoding and Categorical data.
- Display the result for one-hot encode categories and compare it with the documented sample output.
This example of one-hot encode categories processes the sample values in a controlled iteration. It demonstrates One-hot encoding and Categorical data and prints a deterministic result that can be checked against the sample output.
EFFICIENCY
Time and space complexity
O(n · k)
O(n · k)
DEBUGGING CHECKLIST
Common mistakes
For one-hot encode categories, keep the data shape and value types consistent with One-hot encoding.
Keep every dependent statement inside the correct indented Python block.
Verify the final One-hot encoding and Categorical data result against the sample before trying new data.