Python Code
Samples
Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
How to Ping Multiple Hosts in Parallel with Python ThreadPoolExecutor
A parallel host-pinging script using ThreadPoolExecutor and subprocess to check connectivity across multiple addresses concurrently.
import subprocess
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
HOSTS = [
"google.com",
"github.com",
"stackoverflow.com",
"nonexistent.invalid",
"localhost",
]
def ping_host(host: str) -> str:
"""Ping a single host and return a status string."""
result = subp…How to Resize Hundreds of Images in Batch with Python
Resize every image in a folder to a target size using Pillow, creating a new subfolder for processed files.
import os
from PIL import Image
def resize_images_in_batch(directory, output_size=(800, 600)):
if not os.path.exists(directory):
print(f"Directory {directory} does not exist.")
return
output_dir = os.path.join(directory, "resized")
os.makedirs(output_dir, exist_ok=True)
for filename in…How to Run Tesseract OCR from Python with subprocess
This script uses Python's subprocess module to invoke the Tesseract OCR engine from the command line and return the extracted text.
import subprocess
def ocr_image(image_path):
command = ["tesseract", image_path, "stdout"]
result = subprocess.run(command, capture_output=True, text=True)
return result.stdout.strip()
if __name__ == "__main__":
# Stub: call the actual tesseract (must be installed)
text = ocr_image("sample.png")
…How to Split PDF Pages into Ranges in Python
Simulates splitting a PDF into page ranges by validating and returning structured range splits for automation workflows.
import os
def split_pdf_ranges(pdf_name, num_pages, ranges):
"""
Simulates splitting a PDF by returning the page ranges that would be split.
Args:
pdf_name (str): Name of the PDF file.
num_pages (int): Total number of pages in the PDF.
ranges (list of tuple): List of (start, end) …How to Watch a Folder and Convert New Images in Python
Watch a folder for new files and mock-convert images by copying and renaming them in an output directory.
import time
import hashlib
from pathlib import Path
from datetime import datetime
def mock_convert_image(source: Path, dest_dir: Path) -> Path:
"""Mock image conversion: copy bytes and add .converted suffix."""
dest = dest_dir / f"{source.stem}.converted{source.suffix}"
dest.write_bytes(source.read_bytes(…How to apply Kubernetes YAML files from a folder in Python
Uses the Kubernetes Python client to apply all YAML manifests in a directory, with sorted processing and per-file error handling.
import os
import yaml
from kubernetes import client, config
from kubernetes.utils import create_from_yaml
def apply_yaml_folder(folder_path):
"""Apply all YAML files in a folder using the Kubernetes mock client."""
# Load mock configuration
config.load_kube_config()
k8s_client = client.ApiClient()
…How to stage and commit all changes with Git in Python
Run git add -A and git commit from Python using subprocess to automate staging and committing all file changes in one step.
import subprocess
from pathlib import Path
def stage_and_commit_all(commit_message: str) -> None:
"""Stage all changes and create a commit with the given message."""
repo_root = Path.cwd()
if not (repo_root / ".git").exists():
raise RuntimeError("Not inside a Git repository")
subprocess.run([…Run pytest and email summary in Python
Runs pytest via subprocess, extracts the test summary line, and sends it in an email (mocked for demonstration).
import smtplib
import subprocess
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
def run_tests():
"""Run pytest and capture the summary output."""
result = subprocess.run(
["pytest", "-q"],
capture_output=True,
text=True
)
return result.stdo…Track Internet Connectivity and Downtime Automatically in Python
Monitors internet connectivity by pinging a remote host and logs any downtime events with timestamps and duration.
import time
import subprocess
from datetime import datetime
def check_internet(host="8.8.8.8", timeout=3):
"""Returns True if internet is reachable via ping."""
try:
subprocess.run(
["ping", "-c", "1", "-W", str(timeout), host],
capture_output=True,
timeout=timeout …Add a UUID Surrogate Key to Each Row in a CSV with Python
Generate a unique UUID string for every row in a CSV file using the standard-library uuid and csv modules.
import uuid
import csv
def add_surrogate_key(filename):
with open(filename, newline='') as f_in:
reader = csv.DictReader(f_in)
rows = list(reader)
for row in rows:
row['surrogate_key'] = str(uuid.uuid4())
with open(filename, 'w', newline='') as f_out:
writer = csv.DictWri…Build a Python Utility That Detects Duplicate Records Across Multiple Excel Sheets
A Python utility that uses pandas to find overlapping records across different Excel sheets based on specified key columns.
import pandas as pd
from pathlib import Path
def find_duplicate_records_across_sheets(file_path: str, key_columns: list, sheet_names: list) -> dict:
"""
Detect duplicate records across multiple Excel sheets based on specified key columns.
Args:
file_path: Path to the Excel file
key_co…Count Records Processed per Category in Python
Use a Counter dictionary to track how many records of each type (ok, error, retry) were processed in a data pipeline.
from collections import Counter
import random
processed_counter = Counter()
def process_records(records):
for record in records:
processed_counter[record] += 1
return len(records)
if __name__ == "__main__":
sample_records = [random.choice(["ok", "error", "retry"]) for _ in range(10)]
print(f…Create Data Helper Functions in Python for Beginners
Build reusable Python helper functions to load, filter, sort, summarize, and save JSON data — a beginner-friendly starting point for small data pipelines.
import json
from pathlib import Path
from typing import Any, Dict, List
def load_json_file(filepath: str) -> Dict[str, Any]:
"""Load JSON data from a file."""
with Path(filepath).open("r", encoding="utf-8") as file:
return json.load(file)
def filter_by_key(
data: List[Dict[str, Any]], key: str,…ETL in Python: Extract CSV, Transform Dict, Load JSON
Build a simple ETL pipeline in Python that reads a CSV file, transforms each row (stripping whitespace and converting numeric fields), and writes the result to JSON.
import csv
import json
from pathlib import Path
def extract_csv(file_path):
"""Read CSV file and return list of row dictionaries."""
with Path(file_path).open('r', newline='', encoding='utf-8') as f:
reader = csv.DictReader(f)
return list(reader)
def transform_dicts(rows):
"""Transform ro…Group Python Events into Sessions with a Gap Timeout
Groups timestamped events into sessions, starting a new session when the time gap exceeds a specified timeout.
from itertools import groupby
from datetime import datetime, timedelta
def session_window_group(events, gap_seconds=300):
"""Group events into sessions where gap > gap_seconds starts a new session."""
if not events:
return []
events = sorted(events, key=lambda x: x[0])
sessions = []
c…How to Build Data Processing Functions in Python
Create reusable helper functions to load, filter, transform, and aggregate CSV data in Python.
import csv
from pathlib import Path
def load_data(filepath):
"""Load CSV data into a list of dicts."""
with open(filepath, "r", newline="", encoding="utf-8") as f:
return list(csv.DictReader(f))
def filter_rows(rows, column, value):
"""Keep rows where column equals value."""
return [row for…How to Build a Simple Data Pipeline in Python
A beginner-friendly data pipeline that loads JSON, filters records by a field value, and aggregates counts per category.
import json
from pathlib import Path
def load_json(filepath: str | Path) -> list[dict]:
"""Load a JSON file containing a list of records."""
with Path(filepath).open("r", encoding="utf-8") as f:
return json.load(f)
def filter_records(records: list[dict], field: str, value) -> list[dict]:
"""Kee…How to Deduplicate Events with At-Least-Once Delivery in Python
Implements an exactly-once processing pattern for at-least-once event delivery by tracking seen event IDs in a set, skipping duplicates.
seen_ids = set()
def process_event(event_id: str, payload: dict) -> dict:
"""Process an event exactly once, ignoring duplicates."""
if event_id in seen_ids:
return {"status": "duplicate", "event_id": event_id}
seen_ids.add(event_id)
return {"status": "processed", "event_id": event_id, **payloa…How to Filter Data in Python
Filter a list of dictionaries by exact key-value matches or numerical ranges using concise list comprehensions.
from typing import List, Dict, Any
def filter_data(
data: List[Dict[str, Any]], key: str, value: Any
) -> List[Dict[str, Any]]:
"""Return records where data[key] equals value."""
return [record for record in data if record.get(key) == value]
def filter_by_range(
data: List[Dict[str, Any]], key: str…How to List Failed Records in a Dead Letter Queue Mock in Python
A mock Dead Letter Queue stores failed processing records with error details and timestamps, lists them, and exports to JSON.
import json
from datetime import datetime, timedelta
import random
class DeadLetterQueue:
def __init__(self):
self.failed_records = []
def add_failed_record(self, record_id, payload, error_message):
self.failed_records.append({
"record_id": record_id,
"payload": paylo…How to Parse Data in Python: A Beginner's Helper
This helper parses a JSON payload, extracts user names, emails, and signup dates, then summarizes the results.
import json
from datetime import datetime
from typing import Dict, List
def parse_data(payload: str) -> Dict[str, List]:
"""Parse a JSON payload and extract useful fields."""
raw = json.loads(payload)
users = raw.get("users", [])
parsed = {
"names": [],
"emails": [],
"signup_…How to Process CSV Data in Python with a Data Helper
Build a beginner-friendly data helper in Python that loads a CSV file, filters rows by a condition, and summarizes numeric fields.
import csv
from pathlib import Path
DATA = [
{"name": "Alice", "score": 88, "passed": True},
{"name": "Bob", "score": 42, "passed": False},
{"name": "Carol", "score": 95, "passed": True},
]
def load_csv(file_path: Path) -> list[dict]:
with file_path.open(newline="", encoding="utf-8") as f:
r…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.
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 = …How to Track Checkpoint Offset After Batch Commit in Python
A batch processor that tracks the last successfully committed offset after processing records in batches, advancing the checkpoint only when each batch commits successfully.
import json
from typing import Any
class BatchProcessor:
"""Tracks checkpoint offset after committing batches."""
def __init__(self, batch_size: int = 3):
self.batch_size = batch_size
self.offset = 0 # last successfully committed offset (exclusive)
self.total_committed = 0
def …Browse by section
Each section groups closely related Python snippets.
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.