Most SQL problems analysts solve with subqueries can be solved in one line.
Window functions do that.
Here is the simplest way to understand them
A window function performs calculations across rows…
WITHOUT collapsing the dataset like GROUP BY.
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If you work with data in Python, these are the functions you will use on repeat.
That last one. A left join and an inner join look similar.
The results are not. Caught me out more than once.
I’ve never worked with a clean dataset.
Every real project = messy data.
And it always comes down to 4 things:
• Missing values
• Duplicates
• Data types & formatting
• Outliers
Cleaning isn’t a “prep step”. It is the analysis.
Python set operators analysts actually use
You already know sets remove duplicates. But they also do something more useful.
Compare lists without a single loop.
| union -- combine two lists, no duplicates
All customers who bought in January OR February
& intersection -- what appears in both
Customers who churned AND never contacted support
- difference -- what's in one but not the other
Products sold in UAE but not in KSA
^ symmetric difference -- what's exclusive to each side