Most posts here are part of a series, so if one is useful, the one before it probably is too. The SQL for Python Developers series is the place to start if you came for SQL; I Vibe Coded and Lived to Tell if you came for the AI stories.
GROUP BY creates buckets and counts them. It's like Python's `collections.Counter` or pandas `groupby()`. Learn COUNT, SUM, AVG, MIN, and MAX.
Filter rows with conditions, the SQL equivalent of list comprehension `if` clauses. Covers AND/OR, IN, BETWEEN, LIKE patterns, and NULL handling.
Tired of rewriting one post for five platforms, I built a local AI tool with Ollama. What broke, why two passes fixed it, and whether small models can carry it.
SQL returns rows in no guaranteed order; the same query can come back differently twice. ORDER BY gives you control, like Python's sorted() with keys.
SQL's SELECT is more than picking columns. Rename with AS, compute expressions, and use DISTINCT for unique values.
We write SELECT first, but FROM runs first. It's the `for item in collection` part of a Python loop: pick your data source before anything else.
The mental model that makes SQL click: describe what you want instead of looping row by row. Think in sets and the keywords become intuitive.
Your in-memory database disappears when Python exits. Let's fix that with a one-line change that saves everything to disk.
Real SQL practice needs real-looking data. Generate hundreds of customers with Python's Faker library without downloading a single CSV
Install DuckDB with pip, create your first table, insert data, and run a SQL query - all in Python, all in under 5 minutes, zero configuration required