Category: Analytics
Export results and hand off
Finish the job with clean CSV exports, optional Parquet or SQL loads, and short metadata notes for every file. Learn what each destination needs so BI tools and…
A light cleaning pipeline
Turn ad-hoc notebook cells into a light load-clean-validate-summarize pipeline you can trust. Small functions, row-count checks, and cold-start reruns make your Python analytics reproducible for teammates.
Missing data and dtypes
Nulls, empty strings, and bad dtypes break joins and totals in quiet ways. Learn safe pandas casting with coerce, careful fill versus drop, and a symptom-to-fix table you…
Joins and merges in plain English
Learn how pandas merges map to SQL joins, when row counts explode, and how to check keys with validate and indicator. A customers-and-orders walkthrough keeps grain honest before…
Aggregations and groupby
Learn pandas groupby as split-apply-combine, the same idea as SQL GROUP BY. Build sum, mean, count, and multi-metric aggregations, with a clear sales-by-region before and after table example.
