Data and the outside world

Import the statistics box and let it do the maths. Write one function that takes a list of scores and reports the average and the middle value — the five-line averaging loop, replaced by a function someone else already wrote.

Use the datetime box to answer a question that's tedious by hand: how many days until an application closes? Subtract one date from another and let Python count for you.

You know random.randint and random.choice. But what *else* is in that box? Learn the one skill that unlocks every library — ask an AI to list a library's functions — then run two of them yourself to prove they are real.

Real programming starts with a question: "is there already a library for this?" Here are three everyday situations — each one explained in plain words — and your job is to find the Python library that handles it, using an AI and a quick web search.

An AI will happily explain any function — and now and then it is slightly wrong, or invents one that does not exist. Learn the habit that keeps you safe: ask, then run it yourself to check.

Run the whole six-step move on a library nobody taught you. Pick a small problem you actually have, find a box for it, install it, and make something real you can screenshot.

Pick a small dataset, load it with pandas, peek at the rows, count them, and pull out a single column. The smallest honest data workflow.

Take the same CSV from the previous practice. Filter it, sort it, group it. Then write three plain-English findings.

Take a CSV, group it by a category column, and pull out the top five rows from each group. Pandas at its most useful.