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How to Mock train_test_split in Python for Unit Testing
Build a lightweight mock of sklearn's train_test_split to unit test ML pipeline code without needing the full library or deterministic random state.
import numpy as np
from sklearn.model_selection import train_test_split
from unittest.mock import patch
def mock_train_test_split(X, y, test_size=0.25, random_state=None, **kwargs):
"""A simple mock implementation of train_test_split."""
n_samples = len(X)
n_test = int(n_samples * test_size)
n_train =…How to do feature selection with VarianceThreshold in Python
This code demonstrates how to use scikit-learn's VarianceThreshold to remove low-variance features from a NumPy array, keeping only those that vary enough to be useful for modeling.
import numpy as np
from sklearn.feature_selection import VarianceThreshold
def main():
# Mock dataset: 4 samples, 5 features
X = np.array([
[0.1, 0.2, 1.0, 1.0, 0.5],
[0.2, 0.2, 0.0, 1.0, 0.4],
[0.1, 0.2, 1.0, 1.0, 0.6],
[0.3, 0.2, 1.0, 0.0, 0.5]
])
# Select features w…How to ordinal encode categorical data in Python with sklearn
Convert job title categories into ordinal numeric labels using sklearn's OrdinalEncoder with explicit ordering.
from sklearn.preprocessing import OrdinalEncoder
import numpy as np
# Mock data: small job title categories with known ordering
data = np.array([
["intern"],
["junior"],
["mid"],
["senior"],
["lead"]
])
# Define the ordinal order (lowest to highest)
categories = [["intern", "junior", "mid", "seni…StandardScaler mock in Python
A pure-Python StandarScaler class that standardizes features to zero mean and unit variance without sklearn.
import math
class StandardScaler:
def __init__(self):
self.mean_ = None
self.std_ = None
def fit(self, X):
n = len(X)
self.mean_ = [sum(col) / n for col in zip(*X)]
self.std_ = []
for col in zip(*X):
variance = sum((x - self.mean_[i]) ** 2 for i, x …Browse by section
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Guide: free Python code samples library
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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.
How to use this library
- Pick a topic section — strings, lists, files, functions, and more
- Open a sample, read How it works, and copy the code block
- 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.