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Core skills: SQL depth, statistics, Python and dashboards · Advanced beginner · Lesson 23 of 99 · 2 h 40 min video, then about 9 hours of practice
Analysing data with pandas: selecting, grouping, joining and cleaning
After this you can load, filter, clean, group, join and reshape a dataset in pandas and explain how each operation maps to its SQL equivalent.
Why it matters
pandas is SQL and spreadsheets inside Python. Learn it by mapping: boolean filtering is WHERE, groupby().agg() is GROUP BY, merge() is JOIN, pivot_table() is a pivot table. Pay attention to the index (the source of many confusing results), to merge's 'validate' argument which catches unexpected duplicate keys, and to dtypes after loading a CSV. The trap is chaining dozens of operations without checking shapes along the way.
How to use this resource
An analyst-oriented pandas course covering filtering, joins, indexing, cleaning and quick charts.
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