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How to Detect Recently Installed Software in Python
Uses subprocess to call pip and parse package metadata to list recently installed Python packages.
import subprocess
import sys
from datetime import datetime, timedelta
def detect_recently_installed(days=7):
"""Detect recently installed software packages."""
recent_packages = []
cutoff_date = datetime.now() - timedelta(days=days)
try:
# For pip-installed packages (Python packages)
…Scrape HTML Tables in Python with html.parser
Extract data from HTML tables using Python's built-in html.parser module, without third-party dependencies, by overriding callback methods to track table, row, and cell states.
import html.parser
from urllib.request import urlopen
class TableParser(html.parser.HTMLParser):
def __init__(self):
super().__init__()
self.in_table = False
self.in_row = False
self.in_cell = False
self.current_cell = []
self.rows = []
self.row = []
d…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…Check Null Rate Threshold in PySpark DataFrame
This PySpark code checks the null rate of specified DataFrame columns against a threshold and returns violations.
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum, count
def check_null_rate(df, threshold=0.2, columns=None):
"""
Check null rate for specified columns (or all) against a threshold.
Returns columns that exceed the threshold.
"""
cols = columns or df.columns
total…Enrich a stream with reference data by key lookup in Python
Uses streamz to join each incoming record to a reference dictionary by name, adding department and level fields or defaults.
from streamz import Stream
reference = {"alice": {"dept": "eng", "level": 3}, "bob": {"dept": "sales", "level": 5}}
def enrich(record):
name = record.get("name")
ref = reference.get(name)
joined = dict(record)
if ref:
joined.update(ref)
else:
joined["dept"] = "unknown"
joi…Extract Schema.org Structured Data from Any Website in Python
A Python tool that fetches a webpage and extracts all JSON-LD structured data (Schema.org) embedded in <script> tags with type="application/ld+json".
import requests
from bs4 import BeautifulSoup
import json
def extract_schema_org(url):
"""Extract structured data (Schema.org) from a website."""
try:
response = requests.get(url, timeout=10)
response.raise_for_status()
except requests.exceptions.RequestException as e:
return {"err…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 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…How to Implement Slowly Changing Dimension Type 2 History in Python
Build a type-2 slowly changing dimension pipeline that closes old records and opens new ones when customer data changes.
from datetime import datetime, timedelta
def apply_scd_type2(records, current_date):
"""Returns active records after inserting new records with type-2 history."""
history = []
active = {}
for record in records:
key = record["customer_id"]
if key in active:
active[key]["end…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 Validate Fact Table Grain Row Counts in Python
Validate fact table grain by checking dimension key references, unique grain combinations, duplicate rows, and dimension cardinality from a CSV file.
import csv
import hashlib
from pathlib import Path
def validate_fact_grain(fact_file: Path, expected_dim_keys: dict[str, set[str]]) -> dict:
"""
Validate fact table grain by checking each row's dimension keys exist
in expected dimension tables and row count consistency.
"""
dim_references = {}
…How to perform a star schema join in Python
Denormalize mock fact and dimension tables by building lookup dicts and enriching each sales fact with customer, product, and date attributes.
from datetime import date
# Mock dimension tables
customers = [
{"customer_id": 1, "name": "Alice", "city": "New York"},
{"customer_id": 2, "name": "Bob", "city": "Los Angeles"},
{"customer_id": 3, "name": "Carol", "city": "Chicago"},
]
products = [
{"product_id": 101, "name": "Laptop", "category": "…Map Partition Over Chunks in Python with Multiprocessing and Mock
Process data in chunks across multiple CPU cores using multiprocessing Pool.map, and mock the chunk function to test partitioning behavior without heavy computation.
from multiprocessing import Pool
from unittest.mock import patch, Mock
def process_chunk(chunk):
return [x * x for x in chunk]
def map_partition_over_chunks(data, chunk_size, process_func=process_chunk):
chunks = [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)]
with Pool() as pool:
…Pivot long to wide transformation dict
Transform a list of dictionaries from long format to wide format by pivoting on a key column and aggregating values, using pure Python.
def pivot_long_to_wide(rows, key_col, value_col, id_cols=None):
"""
Convert long-format data (list of dicts) to wide format.
Args:
rows: List of dicts in long format
key_col: Column name to pivot on (becomes new column headers)
value_col: Column name whose values become the cel…How to Format Git Patch Series as an MBOX File in Python
Generate a patch-series mbox file from commit metadata with numbered [PATCH nnn/nnn] subjects and a Git-style footer.
import re
from pathlib import Path
def format_patch_series_mbox(commits, output_path="series.mbox"):
entries = []
for idx, commit in enumerate(commits, start=1):
subject = commit["subject"]
author = commit["author"]
email = commit["email"]
date = commit["date"]
body = …How to generate and parse an interactive rebase TODO list in Python
Generate a Git interactive rebase TODO list from commit data and parse it back into structured records.
import re
from collections import namedtuple
Commit = namedtuple("Commit", ["hash", "subject"])
def generate_rebase_todo(commits, action="pick"):
todo_lines = []
for i, commit in enumerate(commits):
if i == 0 and action == "reword":
todo_lines.append(f"reword {commit.hash} {commit.subject…How to mock boto3 S3 upload file wrapper in Python
Wrap an S3 put_object call in a testable function that returns metadata, and mock boto3 to verify the upload without touching AWS.
import boto3
import io
def upload_file_to_s3(file_obj, bucket, key, object_metadata=None):
"""Upload a file-like object to S3 and return a metadata dict."""
s3 = boto3.client("s3")
content = file_obj.read()
s3.put_object(
Bucket=bucket,
Key=key,
Body=content,
Metadata=…How to Speed Up Data Filtering with Python ThreadPoolExecutor
This code compares sequential filtering of even numbers with a threaded version using ThreadPoolExecutor, showing a measurable speedup for I/O-bound work.
import time
from concurrent.futures import ThreadPoolExecutor
import random
def is_even(number):
time.sleep(0.001) # simulate work
return number % 2 == 0
def filter_even_sequential(numbers):
return [n for n in numbers if is_even(n)]
def filter_even_threaded(numbers):
with ThreadPoolExecutor(max_…How to Use threading.local for Per-Thread Data in Python
Use threading.local to keep thread-specific data — each thread gets its own copy of the attribute, so values don't leak between threads.
import threading
import time
local_storage = threading.local()
def worker(name):
local_storage.name = name
time.sleep(0.1)
print(f"Thread {threading.current_thread().name}: {local_storage.name}")
if __name__ == "__main__":
threads = []
for i in range(3):
t = threading.Thread(target=worke…How to Use TypedDict for Data Validation in Python
Define a TypedDict schema and validate raw dictionary input with type hints for safer, more readable data handling.
from typing import Any, Dict, List, Optional, Union, TypedDict, Literal
class Product(TypedDict):
product_id: int
name: str
price: Union[int, float]
in_stock: bool
tags: Optional[List[str]]
def validate_product(data: Dict[str, Any]) -> Product:
product_id: int = int(data["product_id"])
na…How to Validate Data in Python with Typing Hints
Build a runtime validation helper that checks values against Python type hints like Optional, list, and basic types.
from typing import Any, Optional, Union, TypeVar, get_origin, get_args
T = TypeVar("T")
def validate(value: Any, expected_type: type) -> Optional[str]:
"""Returns an error message if value doesn't match expected_type, else None."""
# Handle Optional[...] types
origin = get_origin(expected_type)
if or…Use pytest fixture to mock a database connection in Python
This code shows how to use a pytest fixture and unittest.mock to replace a database connection with a Mock, enabling isolated tests without a real database.
import pytest
import sqlite3
from unittest.mock import Mock
class Database:
def __init__(self, connection):
self.connection = connection
def get_user(self, user_id):
cursor = self.connection.cursor()
cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,))
return cursor.…Build a BFF (Backend for Frontend) Mock Aggregator in Python
A minimal HTTP server implementing the BFF pattern that aggregates user data and orders from two mock backends into a single JSON response.
import json
from http.server import BaseHTTPRequestHandler, HTTPServer
from urllib.parse import urlparse
class MockBackendA:
def get_user(self, user_id):
return {"id": user_id, "name": "Alice", "service": "backend-a"}
class MockBackendB:
def get_orders(self, user_id):
return [
{…How to Build an Anti-Corruption Layer in Python
Wrap a legacy system with a translation layer that converts awkward legacy data into a clean, modern DTO (Data Transfer Object) for use by new code.
class LegacyOrderSystem:
"""Legacy system with awkward, unstructured data."""
def get_order(self):
return {
"order_id": "ORD-123",
"cust": "Acme Corp",
"items": [{"sku": "A1", "qty": 2, "price_each": 10.0}],
"ship_to": "123 Main St, Springfield"
}…Browse by section
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