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How to Download All Assets from GitHub Releases in Python
Downloads every asset attached to the latest GitHub release of a repository, saving them locally using the GitHub API and Python's requests and pathlib libraries.
import requests
import os
import zipfile
from pathlib import Path
def download_github_release_assets(owner: str, repo: str, output_dir: str = "release_assets") -> None:
"""Downloads all assets from the latest release of a GitHub repository."""
releases_url = f"https://api.github.com/repos/{owner}/{repo}/relea…How to Download a GitHub Repository as a ZIP File in Python
Download any public GitHub repository as a ZIP file using the GitHub API and Python's requests and zipfile modules.
import requests
import zipfile
import io
import os
def download_github_repo_as_zip(repo_url, output_path='.'):
"""
Download a GitHub repository as a ZIP file.
Args:
repo_url (str): Full GitHub repository URL (e.g., 'https://github.com/username/repo')
output_path (str): Directory to sa…How to Find Stale GitHub Issues in Python
Filter a list of GitHub issues to find those not updated within a configurable number of days using Python datetime arithmetic.
import os
from datetime import datetime, timezone, timedelta
import re
# Simulated GitHub issue data structure
SAMPLE_ISSUES = [
{"number": 101, "title": "Login button not working", "updated_at": "2025-06-01T12:00:00Z", "assignee": "alice"},
{"number": 102, "title": "Fix database migration error", "updated_at…How to Scan Open Ports on a Host with Python
A Python function that uses socket.connect_ex to check for open TCP ports on a given host within a range and returns a list of open ports.
import socket
def scan_ports(host, start_port, end_port):
open_ports = []
for port in range(start_port, end_port + 1):
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.settimeout(0.5)
result = sock.connect_ex((host, port))
if result == 0:
open_ports.app…How to Track GitHub Stars, Forks, and Watchers in Python
Automatically fetch and track stars, forks, and watchers for multiple GitHub repositories, saving snapshots locally as JSON files for historical analysis.
import os
import time
import json
import requests
from pathlib import Path
from datetime import datetime
REPOS = [
"psf/requests",
"python/cpython",
"pallets/flask",
]
DATA_DIR = Path("github_metrics")
def fetch_repo_stats(repo):
url = f"https://api.github.com/repos/{repo}"
resp = requests.get(ur…How to automatically organize your Downloads folder by file type in Python
This script scans the Downloads folder and moves files into sub-folders based on their extensions (e.g., Images, Documents, Videos).
import os
import shutil
from pathlib import Path
def organize_downloads_folder(downloads_path=None):
if downloads_path is None:
downloads_path = str(Path.home() / "Downloads")
if not os.path.exists(downloads_path):
print(f"Path {downloads_path} does not exist.")
return
fi…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 …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…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…Browse by section
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