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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Python can take repetitive chores off your hands: sorting downloads, renaming a batch of files, collecting items for review, cleaning a CSV, or producing a recurring summary. The first four examples below use Python’s standard library for local files; the final example shows the difference between writing a report and keeping it running on a schedule.
Each script is a starting point you can adapt. Begin with a small test folder or a copy of your data, and read the preview before allowing a script to change files. Python documents the file and path operations used here in its file and directory documentation.
Before running scripts that change files
- Make a test folder with copies of the files you intend to process.
- Start with a specific folder, not your entire home directory or drive.
- Print the proposed changes first, then require an explicit apply option.
- Keep the original CSV and write transformed data to a new file.
- Check the result before deleting or overwriting anything.
These are practical safeguards for scripts that can move or rename files; they are not a special Python requirement.
1. Sort a folder by file type
This script groups files into subfolders named for their extensions—for example, .pdf files go into pdf. It skips folders and files without an extension. By default it only prints a proposed move list; pass --apply to perform the moves.
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from pathlib import Path
import argparse
import shutil
parser = argparse.ArgumentParser()
parser.add_argument("folder", type=Path, help="Folder whose files should be sorted")
parser.add_argument("--apply", action="store_true", help="Perform the proposed moves")
args = parser.parse_args()
root = args.folder.expanduser().resolve()
if not root.is_dir():
raise SystemExit(f"Not a folder: {root}")
moves = []
for item in root.iterdir():
if not item.is_file() or not item.suffix:
continue
destination = root / item.suffix[1:].lower() / item.name
if destination.exists():
print(f"SKIP (destination exists): {item.name} -> {destination}")
continue
moves.append((item, destination))
for source, destination in moves:
print(f"{source.name} -> {destination.parent.name}/{destination.name}")
if args.apply:
for source, destination in moves:
destination.parent.mkdir(exist_ok=True)
shutil.move(str(source), str(destination))
else:
print("Preview only. Run again with --apply to move these files.")Save it as sort_files.py. Run python sort_files.py "path/to/test-folder" to preview, then add --apply only when the list looks right. The script deliberately skips a move if a file with that name already exists in the target folder.
2. Batch-rename files with a preview
Batch renaming is useful when filenames need a consistent prefix or pattern. This example adds invoice_ to every PDF in a chosen folder. It builds and prints the full mapping first, skips conflicts, and changes names only when run with --apply.
from pathlib import Path
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("folder", type=Path)
parser.add_argument("--apply", action="store_true")
args = parser.parse_args()
root = args.folder.expanduser().resolve()
if not root.is_dir():
raise SystemExit(f"Not a folder: {root}")
renames = []
for old_path in sorted(root.iterdir()):
if not old_path.is_file() or old_path.suffix.lower() != ".pdf":
continue
new_path = old_path.with_name("invoice_" + old_path.name)
if new_path.exists():
print(f"SKIP (destination exists): {old_path.name} -> {new_path.name}")
continue
renames.append((old_path, new_path))
for old_path, new_path in renames:
print(f"{old_path.name} -> {new_path.name}")
if args.apply:
for old_path, new_path in renames:
old_path.rename(new_path)
else:
print("Preview only. Run again with --apply to rename these files.")Run it as python rename_pdfs.py "path/to/test-folder", inspect the old-name/new-name pairs, then add --apply to commit them. To use another rule, change the expression that constructs new_path; inspect the resulting mapping before applying it.
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3. Find and copy matching files into a review folder
Use a wildcard pattern when you want to gather likely matches without moving or deleting the originals. This example copies all .pdf files in one folder into a separate review folder. The destination must not be inside the source folder, and existing destination files are left untouched.
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from pathlib import Path
import argparse
import shutil
parser = argparse.ArgumentParser()
parser.add_argument("source", type=Path)
parser.add_argument("destination", type=Path)
parser.add_argument("--apply", action="store_true")
args = parser.parse_args()
source = args.source.expanduser().resolve()
destination = args.destination.expanduser().resolve()
if not source.is_dir():
raise SystemExit(f"Not a source folder: {source}")
if destination == source or source in destination.parents:
raise SystemExit("Choose a destination outside the source folder")
matches = sorted(path for path in source.glob("*.pdf") if path.is_file())
for path in matches:
target = destination / path.name
if target.exists():
print(f"SKIP (destination exists): {path.name}")
else:
print(f"{path.name} -> {target}")
if args.apply:
destination.mkdir(parents=True, exist_ok=True)
for path in matches:
target = destination / path.name
if not target.exists():
shutil.copy2(path, target)
else:
print("Preview only. Run again with --apply to copy matching files.")Run python collect_pdfs.py "path/to/source" "path/to/review" first. Add --apply after checking the matches. Change "*.pdf" to another pattern, such as "*.txt", to adjust the selection. For a nested search, Path.rglob can search subfolders, but then review the list carefully: files in different subfolders can share the same filename.
Python’s tutorial covers wildcard file lists with glob and higher-level file management with shutil.
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4. Clean and summarize a CSV
The standard-library csv module is enough for many plain CSV tasks. This example trims whitespace in every cell, keeps rows whose Status field is Paid, totals their Amount values, and writes the cleaned rows to a separate file. It expects a header row with columns named exactly Status and Amount, and amounts that can be parsed as numbers.
import argparse
import csv
from decimal import Decimal, InvalidOperation
from pathlib import Path
parser = argparse.ArgumentParser()
parser.add_argument("input", type=Path)
parser.add_argument("output", type=Path)
args = parser.parse_args()
input_path = args.input.expanduser().resolve()
output_path = args.output.expanduser().resolve()
if input_path == output_path:
raise SystemExit("Choose a different output path to preserve the original")
try:
with input_path.open("r", newline="", encoding="utf-8-sig") as source:
reader = csv.DictReader(source)
if not reader.fieldnames or not {"Status", "Amount"}.issubset(reader.fieldnames):
raise SystemExit("CSV must have Status and Amount headers")
rows = []
total = Decimal("0")
for row in reader:
cleaned = {key: (value or "").strip() for key, value in row.items()}
if cleaned["Status"].casefold() != "paid":
continue
try:
total += Decimal(cleaned["Amount"])
except InvalidOperation:
raise SystemExit(f"Invalid Amount value: {cleaned['Amount']!r}")
rows.append(cleaned)
except OSError as error:
raise SystemExit(f"Could not read input: {error}")
with output_path.open("w", newline="", encoding="utf-8") as target:
writer = csv.DictWriter(target, fieldnames=reader.fieldnames)
writer.writeheader()
writer.writerows(rows)
print(f"Paid rows: {len(rows)}")
print(f"Paid total: {total}")
print(f"Cleaned rows written to: {output_path}")Run python clean_csv.py "transactions.csv" "paid_cleaned.csv". Review the new file and printed total against a small sample you understand. This is a simple exact-match filter, not a general spreadsheet formula engine. Python’s tutorial notes that CSV is commonly used for exchange with databases and spreadsheets: standard-library tutorial.
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5. Generate a recurring report or reminder
Separate report generation from scheduling: first make a script that can produce one report on demand, then decide how it should run repeatedly. For example, adapt the CSV script above so it writes a dated summary file each time it runs. If the task should run while your script stays open, the third-party schedule package offers a readable in-process API:
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import schedule
import time
from datetime import datetime
from pathlib import Path
def make_report():
# Replace this with a summary of your permitted local input.
report = f"Report generated: {datetime.now().isoformat(timespec='minutes')}n"
output = Path("reports")
output.mkdir(exist_ok=True)
filename = output / f"report-{datetime.now():%Y-%m-%d_%H-%M}.txt"
filename.write_text(report, encoding="utf-8")
print(f"Wrote {filename}")
schedule.every().day.at("09:00").do(make_report)
while True:
schedule.run_pending()
time.sleep(30)Install the dependency with python -m pip install schedule, save the script, and run it with Python. The process must remain running for the job to fire; the example uses the machine’s local time. Stop it with Ctrl+C. The package’s own stable documentation says it is not intended as a one-size-fits-all scheduler.
When the script needs to run unattended
For a job that should run even when you are not watching an open terminal, use the task scheduler provided by your operating system and configure it to launch the Python interpreter with the script. The exact setup differs by operating system and installation, so test the scheduled task manually first and check where its output and errors go. A scheduler can start a script; it does not remove the need to handle missing input files, permissions, network availability, or failures inside the script.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which tasks can stay local?
Sorting, renaming, copying, and reading or writing a CSV can be local operations using Python’s standard library. Python’s Standard Library includes tools such as pathlib, shutil, and csv, and also provides facilities for working with email and other common tasks. The examples here do not send files anywhere.
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Other formats or services may need more setup: Excel workbooks, PDFs, web pages, and service APIs can require an additional package, credentials, or service-specific configuration. Avoid sending confidential data to an external service unless its handling is appropriate for that data. Reusable command-line options can also be built with Python’s argparse module, as used in the file examples above.
Keep learning at your own pace
If you want a guided path beyond these small scripts, Al Sweigart’s Automate the Boring Stuff with Python is available to read online from the author’s official site. It is an optional learning resource, not a prerequisite for adapting the examples.
Quick Recap
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