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#!/usr/bin/env python3 """Rename .jpg files in a folder to YYYY-MM-DD_NNN.jpg using each file's modified date.""" import argparse from datetime import datetime from pathlib import Path def rename_photos(folder: Path, dry_run: bool = False) -> None: photos = sorted( (p for p in folder.iterdir() if p.is_file() and p.suffix.lower() in {".jpg", ".jpeg"}), key=lambda p: p.stat().st_mtime, ) counters: dict[str, int] = {} for photo in photos: day = datetime.fromtimestamp(photo.stat().st_mtime).strftime("%Y-%m-%d") counters[day] = counters.get(day, 0) + 1 target = folder / f"{day}_{counters[day]:03d}{photo.suffix.lower()}" while target.exists() and target != photo: counters[day] += 1 target = folder / f"{day}_{counters[day]:03d}{photo.suffix.lower()}" print(f"{photo.name} -> {target.name}") if not dry_run: photo.rename(target) if __name__ == "__main__": parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("folder", type=Path, help="folder that holds the photos") parser.add_argument("--dry-run", action="store_true", help="show the new names without renaming") args = parser.parse_args() rename_photos(args.folder, args.dry_run) How to run: python3 rename_photos.py ~/Pictures/trip --dry-run Check the printed names, then run it again without --dry-run. Notes: - It uses each file's modified time, not the date the camera recorded. Copied photos may carry the copy date; reading the camera date needs the Pillow library. - Needs Python 3.9 or newer.

import pandas as pd df = pd.read_csv("sales.csv", parse_dates=["date"]) # Amounts like "$1,200.50" become 1200.5; anything unreadable becomes NaN and is reported. df["amount"] = pd.to_numeric(df["amount"].astype(str).str.replace(r"[$,]", "", regex=True), errors="coerce") bad = df["amount"].isna().sum() if bad: print(f"Warning: {bad} rows have an amount that isn't a number and were left out.") df["month"] = df["date"].dt.to_period("M").astype(str) summary = ( df.dropna(subset=["amount"]) .groupby(["region", "month"], as_index=False)["amount"].sum() .sort_values(["region", "month"]) ) summary.to_csv("sales_by_region_month.csv", index=False) print(summary.to_string(index=False)) How to run: pip install pandas python3 sales_summary.py (with sales.csv in the same folder) Notes: - Assumes the date column is in a format pandas can read, such as 2026-09-24. For day-first dates like 24/09/2026, add dayfirst=True to read_csv. - Months are written as 2026-09 so they sort correctly as text.

import random import time from typing import Callable, TypeVar T = TypeVar("T") def retry( func: Callable[[], T], attempts: int = 5, base_delay: float = 0.5, max_delay: float = 8.0, retry_on: tuple[type[BaseException], ...] = (ConnectionError, TimeoutError), ) -> T: """Call func until it succeeds, retrying on the given errors with exponential backoff. Waits base_delay, then twice as long each time (capped at max_delay), with jitter. Re-raises the last error if every attempt fails. """ for attempt in range(1, attempts + 1): try: return func() except retry_on: if attempt == attempts: raise delay = min(max_delay, base_delay * 2 ** (attempt - 1)) time.sleep(delay * random.uniform(0.5, 1.0)) raise AssertionError("unreachable") Example: report = retry(lambda: fetch_report(report_id)) Notes: - The requests library raises its own requests.exceptions.ConnectionError and Timeout, which are not the built-in classes. Pass them in: retry(call, retry_on=(requests.ConnectionError, requests.Timeout)). - Needs Python 3.9 or newer for the tuple[...] hint.