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Day 11 — Python for data analysis basics
Everything so far, applied to a realistic dataset. This is what the first month of an analyst job looks like.
Our dataset
A list of rows, each a tuple. Run this to see it.
The headline numbers
Grouping — the most useful pattern there is
This is the Python equivalent of a PivotTable or a SQL GROUP BY. Learn this shape by heart.
The same thing, shorter, with .get():
Sorting the result
Sorting a list of (amount, name) tuples sorts by amount first — a neat trick for ranking a dictionary.
A complete little report
What comes next
Everything above is plain Python. In real work you would do it with pandas in about three lines. Pandas needs a real Python installation, which is where our course picks up — but understanding the loop underneath is what stops pandas being magic you cannot debug.
Try these yourself
- Total the amounts per city instead of per person.
- Find the single largest order and print who made it.
- Count how many orders each rep made (not the total value).
- Print only reps whose total is above 60000.
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