Automate the Boring Stuff with Python

Every Friday, you download five files, rename them following the same pattern, copy certain values into an overview and move everything into the correct folder. Each step is simple. Together, they take time—and a small careless mistake often emerges only later.

Al Sweigart’s Automate the Boring Stuff with Python starts precisely with such everyday tasks. Programming is taught not as an end in itself, but as a tool for computers to execute repeatable rules reliably. The most important step is often not the code, but understanding the process precisely.

1. Recognise patterns that can be automated

Suitable tasks recur regularly, follow relatively stable rules and have digital inputs and outputs: renaming files, merging spreadsheets, searching text, preparing forms or generating standardised reports.

Not every tedious task should be automated. If it occurs only once, requires many difficult-to-formulate judgements or touches sensitive systems, building the automation can create more effort and risk than manual execution. A good first question is: how often does this happen—and which clearly rule-based part repeats?

2. Break the process into testable steps

Before writing code, describe the process: what inputs are available? Which condition determines the next step? What should be produced? What exceptions might arise? This decomposition is computational thinking on a small scale.

“Create the report automatically” becomes individual steps, for example: find files in the input folder, select only matching filenames, check columns, calculate values, save the result under a new name and report errors. Each step can be understood and tested separately.

3. Small automations need safeguards

A script rapidly repeats not only correct rules, but errors too. Safe testing therefore starts with copies rather than original data, a few examples and visible output. The scope is expanded only once the result is demonstrably correct.

Particular care is needed with deleting, sending, paying or handling personal data. Logs, preview modes, confirmation steps and a recovery option belong in the design. Automation skills therefore mean not just “It runs”, but “I understand, control and limit what runs”.

Your exercise: think of a process in programmable terms

Choose a small digital routine. You do not need code yet. Take three minutes:

1. Minute 1 – Repetition. Note the trigger, frequency and approximate time required. Mark the part that is always the same.
2. Minute 2 – Rule. Write down the input, three to five steps and desired output in simple if–then sentences.
3. Minute 3 – Safety. Which exception is likely? Define a test set, a checkpoint and a way back.

The next step can be a very small script—for example, only displaying filenames before renaming anything at all.

What we take from this

Small automation projects connect digital understanding with problem-solving skills. In WEITER7 , they fit as a manageable experiment: observe the bottleneck, model the process, test with low risk and improve based on actual time saved.

Which digital routine do I repeat so often that it is worth writing down its rule-based core precisely once?

Context: This insight applies selected core ideas from Sweigart’s practical programming book. The exercise and WEITER7 connection are our own applications. Automation can multiply errors and raise security, data-protection or terms-of-use issues. Critical processes should undergo expert review, be version-controlled and first be tested with non-production data.

Sources and further reading: Official book and learning website ↗ · Publisher’s page for the 3rd edition ↗