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How to check Python files for common coding mistakes
Walks a directory tree parsing each .py file with ast, reporting empty functions, bare try blocks, too many parameters, and empty classes.
import ast
import os
import sys
def check_file(filepath):
try:
with open(filepath) as f:
code = f.read()
tree = ast.parse(code, filename=filepath)
except SyntaxError as e:
print(f"{filepath}: SyntaxError: {e.msg}")
return
issues = []
for node in ast.wal…How to generate an htpasswd bcrypt entry in Python
Create a mock htpasswd file entry with a bcrypt-hashed password for a given username using a simple Python script.
import bcrypt
def mock_htpasswd_entry(username, password):
salt = bcrypt.gensalt(rounds=12)
hashed = bcrypt.hashpw(password.encode(), salt).decode()
return f"{username}:{hashed}"
if __name__ == "__main__":
entry = mock_htpasswd_entry("demo_user", "s3cretP@ss")
print(entry)How to generate website performance reports from HTTP requests in Python
Measure and report website load time, status code, and content size using Python's standard library.
import urllib.request
import time
def measure_website_load_time(url):
"""Measures total loading time of a website."""
start_time = time.time()
try:
with urllib.request.urlopen(url, timeout=10) as response:
content = response.read()
status_code = response.status
…How to rename music files by ID3 tags in Python
Renames MP3 files in a folder using artist and title extracted from ID3 tags, with a mock fallback that parses filenames.
import os
import re
from pathlib import Path
def sanitize_filename(name: str) -> str:
return re.sub(r'[<>:"/\\|?*]', '_', name).strip()
def rename_mp3_from_id3(path: Path) -> None:
for f in path.glob("*.mp3"):
# Mock ID3 extraction: derive artist/title from filename
stem = f.stem
if "…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([…How to validate argparse CLI commands in Python
Build a beginner-friendly command-line argument parser with argparse, including required and optional arguments, plus simple validation for age.
import argparse
def main():
parser = argparse.ArgumentParser(description="Validate CLI arguments for beginners.")
parser.add_argument("name", type=str, help="Your name.")
parser.add_argument("--age", type=int, default=None, help="Your age (optional).")
parser.add_argument("--verbose", action="store_t…Stress CPU Threads with a Mock Compute in Python
Simulates CPU-intensive work across multiple threads to test how Python schedules parallel compute.
import threading
import time
def stress_cpu(iterations: int):
result = 0
for i in range(iterations):
result += i * i % 1000
return result
def run_mock_stress(thread_count: int, iterations: int):
threads = []
for tid in range(thread_count):
t = threading.Thread(target=lambda: str…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…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 Clean and Format Data in Python
This code loads JSON data, cleans records by removing empty fields and normalizing text, then summarizes the results with counts and unique keys.
import json
from pathlib import Path
def load_data(filepath: str) -> dict:
"""Load JSON data from a file."""
with Path(filepath).open("r", encoding="utf-8") as f:
return json.load(f)
def clean_records(records: list[dict]) -> list[dict]:
"""Remove empty fields and normalize text to lowercase."""…How to Compress Pipeline Output Gzip Per Partition in Python
Compress each partition of pipeline output into a separate gzip file and verify the compressed data by reading it back.
import gzip
import io
import random
from pathlib import Path
def compress_partition(partition_data: list[str], output_path: Path) -> int:
"""Compress a partition of data to a gzip file, returns bytes written."""
with gzip.open(output_path, 'wt', encoding='utf-8') as f:
f.writelines(partition_data)
…How to Convert Data Types in a Python Data Pipeline
Demonstrates a simple Python data pipeline that converts string values to proper types (bool, int, float, datetime) and outputs structured JSON.
import json
from datetime import datetime
def convert_value(value):
"""Convert string values to appropriate Python types."""
if value.lower() == "true":
return True
if value.lower() == "false":
return False
if value.isdigit():
return int(value)
try:
return float(val…How to Count Events by Minute with a Tumbling Window in Python
Group timestamps into fixed 60-second tumbling windows and count events per bucket using a dict.
from collections import defaultdict
from datetime import datetime, timedelta
def tumbling_window_count(events, window_seconds=60):
buckets = defaultdict(int)
for event in events:
ts = datetime.fromisoformat(event["timestamp"])
bucket_start = ts - timedelta(seconds=ts.second % window_seconds,
…How to Count JSON Records in Python
Read a JSON file and count the number of top-level records, handling both list and dictionary structures.
import json
from pathlib import Path
def count_records(json_file):
"""Count top-level records in a JSON file."""
with open(json_file, "r") as f:
data = json.load(f)
# Handle both list of records and dict of records
if isinstance(data, list):
return len(data)
elif isinstance(da…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 Explode an Array Field into Multiple Rows in Python
This code flattens a list of dictionaries by exploding each array field value into its own row, duplicating the other fields as needed.
from collections import defaultdict
data = [
{"id": 1, "name": "Alice", "tags": ["python", "data", "ai"]},
{"id": 2, "name": "Bob", "tags": ["web", "devops"]},
{"id": 3, "name": "Carol", "tags": []},
]
def explode_array_field(records, array_field):
result = []
for record in records:
for v…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 Find Missing Values in Large Datasets in Python
Analyze missing values across multiple large pandas DataFrames with counts and percentages.
import pandas as pd
import numpy as np
def find_missing_values_summary(datasets):
"""Analyze missing values across multiple datasets (dict of name: DataFrame)."""
summary = {}
for name, df in datasets.items():
missing_count = df.isnull().sum()
total_rows = len(df)
missing_pct = (mi…How to Group Data by Key in Python
Group a list of dictionaries by a specified key using a defaultdict and compute per-group averages.
from collections import defaultdict
def group_by_key(data, key):
grouped = defaultdict(list)
for item in data:
grouped[item[key]].append(item)
return dict(grouped)
if __name__ == "__main__":
records = [
{"name": "Alice", "dept": "Engineering", "score": 85},
{"name": "Bob", "de…How to Group Rows by Key into Nested Arrays in Python
This code groups rows in a list of dictionaries by a specified key and returns a dictionary with each key mapped to a list of values from another key.
from collections import defaultdict
def implode_rows(rows, key, value_key):
grouped = defaultdict(list)
for row in rows:
grouped[row[key]].append(row[value_key])
return dict(grouped)
if __name__ == "__main__":
data = [
{"category": "fruit", "item": "apple"},
{"category": "fr…How to Hash Email Addresses in a PII Masking Pipeline in Python
Replaces every email address in a text string with its SHA-256 hash to protect personally identifiable information (PII).
import hashlib
import re
def hash_email(email: str) -> str:
"""Mask an email address by hashing it with SHA-256."""
normalized = email.strip().lower()
return hashlib.sha256(normalized.encode("utf-8")).hexdigest()
def mask_pii_emails(text: str) -> str:
"""Replace all email addresses in text with their…How to Implement Incremental Load with Watermark by updated_at in Python
Load only new or changed rows into SQLite by comparing an updated_at timestamp against a stored watermark, returning counts and the new watermark.
import sqlite3
from datetime import datetime, timedelta
def watermark_incremental_load(db_path, table_name, last_watermark, source_data):
"""Load only rows with updated_at greater than the last watermark."""
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
# Create table if it doesn't exist
…How to Implement SCD Type 1 Overwrite in Python with SQLite
Implement SCD Type 1 dimension updates in Python using SQLite — overwrite existing rows with new data while preserving keys.
import sqlite3
# Simulate a dimension table with SCD Type 1 (overwrite)
conn = sqlite3.connect(":memory:")
cursor = conn.cursor()
# Create dimension table
cursor.execute("""
CREATE TABLE customer_dim (
customer_id INTEGER PRIMARY KEY,
customer_name TEXT,
city TEXT,
updated_at TEXT…Browse by section
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