groupby
groupby at Beyond Market Intelligence is a file of 3 stories. The newest of them: “Discover How Excel Enthusiasts Built a Space Shooter and Solved Finance Workflows”, “Discover how PySpark window functions transform data analysis beyond groupBy”, and “Run Your Data Analysis Where Your Data Lives”. A 120 FPS space shooter built entirely in Excel VBA? Grouping data in PySpark gets you partway there, but it leaves you staring at a wall when you need rankings, running totals, or lagged values. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every groupby story on Beyond Market Intelligence, newest first.
Discover How Excel Enthusiasts Built a Space Shooter and Solved Finance Workflows
A 120 FPS space shooter built entirely in Excel VBA? That's not just clever, it's a reminder that this tool holds more power than most users tap into. The week's top post proves playfulness can coexist with productivity. Meanwhile, finance beginners got practical advice: learn XLOOKUP and SUMIFS. For deeper coverage on cleaning messy data before it skews your models, see our related article on catching AI slop.
Discover how PySpark window functions transform data analysis beyond groupBy
Grouping data in PySpark gets you partway there, but it leaves you staring at a wall when you need rankings, running totals, or lagged values. That's where window functions step in, and this practical guide shows you exactly why the trusty `groupBy` falls short. It's a clear, hands-on read for anyone ready to move past basic aggregations. If you're also curious how structured thinking applies elsewhere, our piece on paragraph structure in LLMs pairs nicely with this mindset.
Run Your Data Analysis Where Your Data Lives
Most dataframe work pulls data out of the database, then pushes it back after Python finishes its part. memFrame flips that script. It compiles your Python/DataFrame API calls directly into SQL, letting DuckDB, PostgreSQL, or ClickHouse do the heavy lifting where the data lives. That is a smarter default. The incremental release strategy is also a good discipline: get inspection, cleaning, and arithmetic solid before tackling groupby and window functions. For deeper Python performance thinking, our guide on advanced techniques pairs well here.