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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
How to Count Items with Default Parameters in Python
Define a Python function that prints each item with a running counter, using default parameters to allow custom start values and step increments.
def count_items(items, start=0, step=1):
"""Count items in a list with configurable start value and step."""
count = start
for item in items:
print(f"{count}: {item}")
count += step
if __name__ == "__main__":
fruits = ["apple", "banana", "cherry"]
print("Default parameters (start=0…How to Detect the Recursion Limit in Python with sys.getrecursionlimit
This Python code recursively calls itself, printing the current recursion depth and the recursion limit from sys.getrecursionlimit, and catches the RecursionError when the limit is hit.
import sys
def recurse(depth=0):
print(f"Depth: {depth}, Recursion limit: {sys.getrecursionlimit()}")
return recurse(depth + 1)
if __name__ == "__main__":
try:
recurse()
except RecursionError:
print("Recursion limit reached!")
print(f"Final recursion limit: {sys.getrecursionli…How to Print an Exception Chain in Python for Debugging
A helper that walks an exception's __cause__ and __context__ chain, printing each level with indentation to make debugging nested errors clearer.
import sys
import traceback
def pretty_exception_chain(exc):
"""Print the full exception chain with cause/context details."""
chain = []
current = exc
seen = set()
while current is not None and id(current) not in seen:
seen.add(id(current))
chain.append(current)
curren…How to Watch a Directory for New Files in Python
Poll a directory at regular intervals and detect newly added files, printing each one as it appears.
import time
import os
from pathlib import Path
WATCH_DIR = Path("watched_files")
def watch_for_new_files(directory: Path, sleep_time: float = 1.0, max_iterations: int = 10):
"""Poll a directory for new files and print when one appears."""
directory.mkdir(exist_ok=True)
existing = set(os.listdir(directory…How to Check Data Type and Inspect Dictionaries and Sets in Python
Inspect dictionaries and sets by printing their contents, types, and sizes using a small helper function.
def check_data(data):
"""Helper to inspect dictionaries and sets."""
if isinstance(data, dict):
print(f"Dictionary with {len(data)} keys")
for key, value in data.items():
print(f" {key}: {value} ({type(value).__name__})")
elif isinstance(data, set):
print(f"Set with {le…Count Data in Python with Comprehensions and Generators
Count list items with a dict comprehension and generate squares lazily with a generator expression, printing both results.
from collections import Counter
data = ["apple", "banana", "apple", "cherry", "banana", "apple"]
counts = {item: data.count(item) for item in set(data)}
square_gen = (x * x for x in range(5))
squares = list(square_gen)
if __name__ == "__main__":
print("Manual count:", counts)
print("Counter:", dict(Counter…Group Consecutive Keys in Python with itertools.groupby
Group consecutive equal elements in a list using the itertools.groupby generator, printing each key and its values.
from itertools import groupby
data = [1, 1, 2, 2, 3, 1, 1, 4, 4, 4]
for key, group in groupby(data):
group_list = list(group)
print(f"Key: {key}, Values: {group_list}")Detect Circular Imports Across Python Projects Automatically
This script walks through all .py files in a directory, builds an import graph, and uses depth-first search to find cycles—printing each circular dependency chain.
import ast
import sys
from pathlib import Path
from collections import defaultdict, deque
def find_imports(filepath):
"""Return set of module names imported by a Python file."""
imports = set()
try:
with open(filepath) as f:
tree = ast.parse(f.read())
except (SyntaxError, UnicodeDe…How to Compare Two GitHub Repositories and Highlight Differences in Python
Fetch metadata from two GitHub repositories using the GitHub API and compare key attributes like stars, forks, license, and language, printing any differences.
import requests
import json
from pathlib import Path
def fetch_repo_data(owner, repo_name):
"""Fetch repository metadata from GitHub API."""
url = f"https://api.github.com/repos/{owner}/{repo_name}"
response = requests.get(url)
response.raise_for_status()
return response.json()
def compare_repos(…How to Recover Deleted .txt Files from a Backup in Python
A Python function that searches a backup directory recursively and copies all .txt files to a destination folder, printing each recovered file name and a total count.
import os
import shutil
from pathlib import Path
def recover_deleted_txt_files(source_backup_dir: str, destination_dir: str) -> None:
"""Recover .txt files from backup directory."""
backup_path = Path(source_backup_dir)
dest_path = Path(destination_dir)
dest_path.mkdir(parents=True, exist_ok=True)
…Mock Certbot Renewal in Python for Testing
Simulates a Let's Encrypt certificate renewal by writing a mock certificate file and printing realistic certbot CLI output, without calling the actual certbot.
import subprocess
import sys
from datetime import datetime, timedelta
from pathlib import Path
def renew_cert(domain: str, output_dir: str = "certs") -> str:
"""Simulate a Let's Encrypt renewal with mock certbot output."""
out = Path(output_dir)
out.mkdir(parents=True, exist_ok=True)
cert_path = out…Python Exponential Backoff Retry Example
Retry a flaky function with exponential backoff and jitter-free delays, printing each attempt and finally returning the successful result.
import random
import time
def flaky_function():
if random.random() < 0.6:
raise ConnectionError("Temporary network error")
return "success"
def retry_with_exponential_backoff(func, max_retries=5, base_delay=1.0):
for attempt in range(max_retries + 1):
try:
return func()
…How to Create a Rich Console Progress Bar Mock in Python
This code uses Rich's Console and Progress API to build a simulated progress bar for a long-running task, updating progress and printing status messages.
import time
from rich.console import Console
from rich.progress import Progress, BarColumn, TextColumn, PercentageColumn
console = Console()
def run_simulation():
console.print("[bold cyan]Starting simulated task...[/bold cyan]")
with Progress(
TextColumn("[bold blue]{task.description}[/bold blu…How to Mock a pipx Install Command in Python
Simulate a pipx install step by validating tool names and printing the exact command output a real pipx run would produce.
import subprocess
import sys
def install_with_pipx(tool_name: str) -> str:
"""
Mock a pipx install step by validating the tool name and
simulating the installation command output.
"""
allowed_tools = {"black", "flake8", "mypy", "ruff"}
if tool_name not in allowed_tools:
raise ValueErr…How to Validate Data with a Simple Dict-Based Rules Helper in Python
Validates a dictionary against a set of callable rules, printing pass/fail per field and returning an overall boolean.
import json
from pathlib import Path
from typing import Any, Callable
def validate_data(
data: dict[str, Any],
rules: dict[str, Callable[[Any], bool]],
path: Path | None = None,
) -> bool:
"""Validate a dict against a set of simple rules."""
all_valid = True
for field, validator in rules.item…Lint a Dockerfile with a Mock Hadolint in Python
A lightweight Python script that simulates hadolint by scanning Dockerfile text for common lint rules and printing violations.
import subprocess
import tempfile
from pathlib import Path
def lint_dockerfile(content: str) -> list[str]:
"""Mock hadolint by checking a few rules and returning violations."""
violations = []
lines = content.splitlines()
for idx, line in enumerate(lines, start=1):
stripped = line.strip()
…Makefile Targets for lint, test, and build in Python
This Python script defines common Makefile targets (lint, test, build) as subprocess commands, printing each target's command and executing them with error checking.
import subprocess
TARGETS = {
"lint": ["ruff", "check", "."],
"test": ["pytest", "-q"],
"build": ["python", "-m", "build"],
}
def run(target: str) -> None:
if target not in TARGETS:
raise ValueError(f"Unknown target: {target}")
print(f"Running {target}...")
subprocess.run(TARGETS[tar…How to Profile CPU Hot Path in Python with cProfile and sort_stats cumtime
Profile a Python function's CPU usage by running cProfile, sorting stats by cumulative time, and printing a readable report to stdout.
import cProfile
import pstats
import io
def slow_function():
total = 0
for i in range(100_000):
total += i * i
return total
def fast_function():
return sum(i for i in range(100))
def main():
slow_function()
fast_function()
if __name__ == "__main__":
profiler = cProfile.Profi…How to Use ThreadPoolExecutor and ProcessPoolExecutor in Python
Compares ThreadPoolExecutor and ProcessPoolExecutor by running CPU-bound and I/O-tolerant tasks over a large list, printing elapsed times and first results.
import time
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import math
numbers = list(range(1, 1000001))
def compute_square(n):
return n * n
def compute_sqrt(n):
return math.sqrt(n)
def run_executor(executor, func, data):
start = time.perf_counter()
results = list(executo…How to spawn multiple worker processes in Python with multiprocessing.Process
Spawns three separate worker processes using multiprocessing.Process, runs them concurrently, and waits for all to finish before printing a completion message.
import multiprocessing
import time
def worker(name):
print(f"Worker {name} started")
time.sleep(1)
print(f"Worker {name} finished")
return name
if __name__ == "__main__":
processes = []
for i in range(3):
p = multiprocessing.Process(target=worker, args=(i,))
processes.append(p…Check if a Timestamp Falls in a Daily Maintenance Window in Python
A small Python function that returns True when a datetime falls inside a daily maintenance window, and a demo printing yes/no for sample timestamps.
from datetime import datetime, timedelta
from zoneinfo import ZoneInfo
def in_maintenance_window(now: datetime, start_hour: int = 2, duration_hours: int = 4) -> bool:
"""Return True if 'now' falls inside the daily maintenance window."""
day_start = now.replace(hour=start_hour, minute=0, second=0, microsecond…How to Stage ML Model Workflows with Python Classes
Defines a Stage class to model ML pipeline stages with variants and mocks, printing grammar for Model, Staging, and Production stages.
class Stage:
def __init__(self, name):
self.name = name
self.mocks = []
self.variants = []
def add_mock(self, mock_name):
self.mocks.append(mock_name)
def add_variant(self, variant_name, productions=()):
self.variants.append((variant_name, list(productions)))
…How to Train a Gradient Boosting Regressor in Python
Build and evaluate a scikit-learn GradientBoostingRegressor on a synthetic dataset, printing test MSE and feature importances.
import numpy as np
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error
def train_gradient_boosting_mock():
# Toy regression dataset
np.random.seed(42)
X = np.random.rand(100, 3) * 10
y = 2 * X[:, 0] - 1.5 * X[:, 1] + 0.5 * X[:, 2] + np.random.normal(0,…Train Logistic Regression From Scratch in Python
Trains a binary logistic regression model using gradient descent on mock data, printing learned weights and probabilities.
import numpy as np
# Mock data: 2 features, binary classification
X = np.array([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
y = np.array([0, 0, 1, 1, 1])
# Add bias term (column of ones)
X_b = np.c_[np.ones((X.shape[0], 1)), X]
# Initialize parameters
theta = np.zeros(X_b.shape[1])
# Hyperparameters
learning_rate = 0…Browse by section
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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.
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.