Reference library

Python Code Samples

Medium snippets you can copy, study, and run in the browser editor.

3 matches
Automation & scripting medium

Automatically Generate Charts from CSV Files with One Command

Read a CSV file with headers, extract the first two numeric columns, and save a matplotlib line chart as a PNG image.

csv matplotlib charting
Python
import csv
import sys
from pathlib import Path
import matplotlib.pyplot as plt

def generate_chart(csv_path: str) -> None:
    """Read a CSV file with headers and plot the first two numeric columns."""
    data = []
    with open(csv_path, 'r', newline='') as f:
        reader = csv.reader(f)
        headers = next(re…
64 0 Open
Concurrency & performance medium

How to Share Memory Between Processes in Python with multiprocessing.Value and Array

Share a numeric value and a list-like array across multiple Python processes using multiprocessing.Value and multiprocessing.Array, with each process modifying the same memory.

multiprocessing shared-memory concurrency
Python
import multiprocessing

def worker(shared_value, shared_array, index):
    shared_value.value += 10
    shared_array[index] = shared_array[index] * 2

if __name__ == "__main__":
    shared_value = multiprocessing.Value("i", 5)
    shared_array = multiprocessing.Array("i", [1, 2, 3, 4, 5])

    processes = []
    for i…
13 0 Open
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

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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.

How to use this library

  1. Pick a topic section — strings, lists, files, functions, and more
  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.