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Python Code Samples

Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.

2 matches
ML engineering pipelines medium

How to Build an sklearn Pipeline with ColumnTransformer in Python

A mock example showing how to chain preprocessing and a regression model into a single sklearn Pipeline, scaling numeric features and one-hot encoding categorical features with ColumnTransformer.

sklearn pipeline columntransformer
Python
import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression

# Mock dataset
X = np.array([[1, 'red'], [2, 'blue'], [3, 'red'], [4, 'green'], [5, 'blue']], dtype=o…
13 0 Open
ML engineering pipelines easy

One Hot Encode Categories in Python

Convert a list of categorical strings into one-hot encoded numeric vectors using pure Python and NumPy.

one-hot encoding categorical numpy
Python
import numpy as np

categories = ["red", "green", "blue", "red", "blue", "green", "red"]

unique = sorted(set(categories))
lookup = {cat: i for i, cat in enumerate(unique)}

one_hot = []
for cat in categories:
    row = [0] * len(unique)
    row[lookup[cat]] = 1
    one_hot.append(row)

print("Categories:", categories…
13 0 Open

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Guide: free Python code samples library

Copy-ready Python snippets for learners and developers

PythonSkillset code samples are short, focused examples organised by topic and difficulty. Every snippet is server-rendered HTML — readable by search engines and easy to copy. Open any sample, read the notes, copy the code, then press Try in editor to run it in the browser with Pyodide.

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  2. Open a sample, read How it works, and copy the code block
  3. Run it in the IDE, tweak values, then take a related quiz or tutorial lesson

Samples vs tutorials and challenges

Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.