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Re:What SQL Analysts Need to Know About Python (2016) by bshipp on Monday November 04, 2019 @03:55PM (#21446412)
You're absolutely right. As someone coming into Python and SQL from a purely Excel background, the first realization I had was that I had been, in essence, programming Excel. Not in a rational VBA kind of way, but in how I was linking all of my cells (and sheets and books) and then using pivot tables, etc., to extend Excel beyond a single column/row perspective. Microsoft PowerPivot (or whatever it used to be called) was my gateway drug into the world of SQL.

I can generate similar utilities to Excel/PivotTables using Plotly Dash or Pandas or even DataTables, but even after a few years of learning and practising it would take me less than 1/10th the time to generate an equivalent excel pivot and chart and have it displayed in front of a group.

I'm not saying it's better, just that those execs are so comfortable with that visual-feedback excel approach that it'll be an uphill battle to convert them to a programmatic one. Pivot tables provide very rapid means of filtering, modifying output, and aggregating information than is otherwise possible inside Excel. To those coming from a two dimensional excel spreadsheet world, discovering pivot tables is like viewing the world in 3D.

I don't see why you would go to Excel pivot tables from SQL; you already have a more powerful tool at your disposal, if you're comfortable with it. Going from Excel to SQL? That hurdle is a bit higher.

Re:What SQL Analysts Need to Know About Python (2016) by iagovar on Monday November 04, 2019 @06:39PM (#21447978)
Well, because Excel is quick. I know R and SQL, and I still use Excel a lot.

If I want a quick overview of a problem, query db from Excel -> PowerQuery -> PivotTables. It's easy to use, it's very easy to find solutions in google for PowerQuery, and it just feels slick.

If I need in depth analysis then is when I switch to programming.