Python automation: stop doing repetitive work
Gjethe writes Python automation that replaces manual work: generating reports, cleaning and merging spreadsheets, syncing databases, processing files and PDFs, monitoring prices and stock, and connecting tools that don't integrate. Scripts run on a schedule in the cloud with logging and alerts, and come with documentation your team can follow. Pricing is based on your project and agreed in your quote.
- Free first consultation
- Written quote for your project
- Flexible payment milestones
At a glance
- Language
- Python
- Runs on
- Cloud functions, cron, containers
- Includes
- Logging, alerts, documentation
- Pricing
- Quoted per project
Definition
What is Python automation?
Python automation means writing scripts in the Python programming language that perform repetitive computer tasks (moving data, transforming files, calling APIs, generating documents) automatically and on a schedule.
If someone on your team copies data between spreadsheets, builds the same report every Monday or renames files by hand, that work can almost certainly be scripted. Python is ideal for it: excellent libraries for data, files, spreadsheets and web APIs, and easy to read when someone needs to change it later. We focus on scripts that are boring in the best sense. They run, they log what they did, and they tell you when something goes wrong.
Who is it for?
Spreadsheet-heavy teams
Data merged, cleaned and reformatted by hand every week across several Excel or Google Sheets files.
Analysts and managers
Reports assembled manually from exports that could be generated and emailed automatically.
E-commerce operators
Price, stock and competitor monitoring, feed generation and order exports.
Document-heavy processes
PDFs to split, merge, rename, read or generate in bulk.
What's included, and what you keep
What's included
- Task analysis and specification
- Python script or small service development
- Spreadsheet and CSV processing with pandas
- API integrations with your tools
- PDF, Word and Excel reading and generation
- Scheduling via cloud functions or cron
- Configuration without code changes
- Logging of every run
- Email or Slack alerts on failure
- Unit tests for core logic
- Deployment to your cloud account
- Documentation and handover
What you receive
Handed over at the end. Yours to keep.
- Working, scheduled automation
- Source code in your repository
- Run logs and alerting
- README with set-up and usage
- Configuration file for settings you may change
Examples of Python automation
Automated reports
Pull data from your tools every morning, build an Excel or PDF report and email it to the team.
Spreadsheet clean-up
Merge exports from several systems, standardise formats, remove duplicates and flag errors.
Data sync
Keep a database, CRM and spreadsheet in step without manual exports.
Document generation
Produce contracts, certificates or invoices from templates and data in bulk.
Python script or no-code workflow?
No-code tools such as Zapier and Make are excellent for simple hand-offs between apps that your team wants to edit themselves. They become awkward when tasks involve heavy data processing, complex logic, large files or volumes where per-task pricing adds up.
Python handles those cases comfortably and costs little to run. The trade-off is that changes need someone who can read code, which is why we write clear scripts, put settings in configuration files and document how they work. Often the best set-up combines both: a no-code trigger that hands heavy lifting to a Python function.
Signs a task should be scripted
- It happens at least weekly
- The steps are the same every time
- It involves copying between files or systems
- Mistakes in it cause real problems later
- Only one person knows how to do it
- It's done outside working hours to meet a deadline
How do we make scripts easy to live with?
Every script we deliver follows the same habits: settings in one configuration file, a clear log line for each run, alerts that say what failed and why, and a README that explains how to run, change and pause it.
We also design for the day something unexpected arrives, a new column, an empty file, a supplier changing its format. The script stops safely and tells you, rather than producing wrong numbers that someone only notices weeks later.
How Python automation works, step by step
Every step has a clear output you can review. Timing is agreed in your quote.
- 1
Shadow the task
We watch the manual process once and collect example inputs and outputs.
- 2
Specify
Inputs, rules, outputs, schedule and what should happen with bad data.
- 3
Build
Script developed with tests against your real examples.
- 4
Deploy
Scheduled in the cloud under your account with logging and alerts.
- 5
Parallel run
The script runs alongside the manual process until results match.
- 6
Handover
Documentation and a short walkthrough for whoever looks after it.
Tools, and what shapes your quote
Pricing is based on your project and agreed in your quote. These are the things that move it.
| Factor | Why it matters |
|---|---|
| Number of data sources | Each system or file format the script reads adds work. |
| Data quality | Messy, inconsistent inputs need more validation and exception handling. |
| Output complexity | A formatted PDF report takes longer than a CSV export. |
| Schedule and reliability needs | Hourly, monitored runs need more infrastructure than a weekly script. |
| Interface | A simple web form to trigger the script adds a small front end. |
| Area | Tools |
|---|---|
| Core libraries | pandas, openpyxl, requests, pydantic |
| Documents | pypdf, pdfplumber, python-docx, ReportLab |
| Web | Playwright, BeautifulSoup, REST and GraphQL clients |
| Scheduling & hosting | AWS Lambda, Google Cloud Functions, cron, Docker |
| Data stores | PostgreSQL, SQLite, Google Sheets, Airtable |
Common mistakes to avoid
What we see go wrong most often, and how a good project avoids it.
Scripts on someone's laptop
Automation that only runs when one person's computer is on stops the day they go on holiday.
Hard-coded settings
File paths, emails and thresholds belong in configuration, so changes don't require a developer.
No log of what happened
Without logs, nobody can tell whether last night's run worked or why it didn't.
Skipping the parallel run
Compare automated results with the manual process before switching it off.
Frequently asked questions
Where does the script run?
On a scheduled cloud function or small server under your account, with logs and alerts so you know it ran.
Python or a no-code tool like Zapier?
No-code tools are great for simple hand-offs between apps. Python is better for heavy data processing, custom logic or volumes that make per-task pricing expensive. We often combine them.
Can non-technical staff run the script?
Yes. Scripts can run automatically, from a button in a simple web page or by dropping a file into a shared folder.
Can it work with Excel files?
Yes, reading, cleaning, merging and producing formatted Excel workbooks, including formulas and charts.
What happens if the input data changes format?
The script validates inputs and alerts you when something unexpected arrives, instead of producing wrong output silently.
Can you automate tasks in websites without an API?
Sometimes, with browser automation. It's more fragile than an API, so we use it only when there's no better option and monitor it closely.
Do you maintain scripts after delivery?
Yes, on a maintenance plan, or we document them so your team or another developer can take over.
Can the script use AI?
Yes, for example to read unstructured documents or classify records, with validation of the AI's output.
How do you keep credentials secure?
Passwords and API keys are stored in a secret manager or environment variables, never in the code.
Who owns the code?
You do. It lives in your repository and runs on your accounts.
Can you turn our Excel macros into Python?
Yes. VBA macros that have grown fragile can be rewritten in Python, which is easier to test, schedule and run without opening Excel.
Can scripts send their results to Slack or Teams?
Yes. Reports, summaries and alerts can be posted to a channel, emailed or saved to a shared folder.
Can a script watch a folder and process new files?
Yes. Scripts can watch a shared drive, email inbox or cloud storage bucket and process each new file as it arrives.
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Tell us what you need. You get an honest assessment, a clear plan and a written quote. The first consultation is always free.
- What happens next
- 1You tell us what you need
- 2We aim to reply within 24 hours with questions
- 3You get a written quote for your project