Reference library

Python Code Samples

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

6 matches
Files & data medium

Find Duplicate Web Pages by Content Similarity in Python

Compute SHA-256 hashes of file contents to detect and report duplicate HTML pages or any files in a directory.

duplicate-detection hashing sha256
Python
import hashlib
import os
from collections import defaultdict

def get_file_hash(filepath):
    """Compute SHA-256 hash of file contents."""
    sha256 = hashlib.sha256()
    with open(filepath, 'rb') as f:
        for chunk in iter(lambda: f.read(4096), b''):
            sha256.update(chunk)
    return sha256.hexdiges…
46 0 Open
Files & data medium

How to Build a CSV Comparison Tool That Highlights Every Changed Cell in Python

Read two CSV files with DictReader, compare cell by cell, and return a list of dictionaries describing each changed cell using only the standard library.

csv comparison diff
Python
import csv
from pathlib import Path

def csv_cell_diff(file_a: str, file_b: str) -> list[dict]:
    rows_a = list(csv.DictReader(Path(file_a).open('r', newline='')))
    rows_b = list(csv.DictReader(Path(file_b).open('r', newline='')))
    if not rows_a or not rows_b:
        return []
    columns = list(rows_a[0].key…
40 0 Open
Files & data medium

How to Merge Sorted Chunk Files in Python

Merge multiple sorted text files into one sorted output file using a heap for efficient k-way merging.

heapq merge-sort external-sort
Python
import heapq


def merge_sorted_chunks(chunks, output_path):
    """Merge multiple sorted iterables into single sorted output file."""
    with open(output_path, "w") as out_f:
        # Open all chunk files
        handles = [open(chunk, "r") for chunk in chunks]
        try:
            # Heap of (value, index) tupl…
14 0 Open
Files & data medium

How to Stream Large CSV Files in Python

Process a large CSV file in memory-efficient chunks using Python's csv module, yielding batches of rows instead of loading everything at once.

csv streaming memory-efficient
Python
import csv
from pathlib import Path

def process_csv_in_chunks(file_path, chunk_size=1000):
    """Yield rows from a large CSV file in chunks without loading all into memory."""
    with open(file_path, 'r', newline='') as f:
        reader = csv.DictReader(f)
        chunk = []
        for row in reader:
            …
12 0 Open
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
Data pipelines & processing medium

How to Stream a Large JSONL File Line by Line in Python

Process a large JSON-lines file incrementally using streaming techniques to avoid loading the entire file into memory.

streaming jsonl large-files
Python
import json

def process_large_file(filepath, chunk_size=8192):
    """
    Stream a large JSON-lines file line by line, processing each record
    without loading the entire file into memory.
    """
    total_count = 0
    total_sum = 0
    
    with open(filepath, 'r') as f:
        while True:
            chunk = …
13 0 Open

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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

  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.