Step 1: Ask the question and load the data

In this project you play a data analyst. A shop manager gives you a file of sales (13 rows from four regions, January to March) and asks: How much did we sell, where, and what sells best? You will load and clean the data, summarise it, find the best sellers, and print a tidy report with Python, then cross-check ideas with SQL.

Always start by looking at the raw data. Real data is messy. Python's csv module reads text into dictionaries, one per row, using the header line as the keys. Run it and look closely at rows 3 and 5.

Notice two problems already: row 3 has spaces around East, and row 5 has n/a where the number of units should be. Cleaning data (step 2) is usually the biggest part of an analysis.