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Python for Professionals — How Far Have You Come? — Python For Professionals

Python for Professionals — How Far Have You Come? — Python For Professionals | zuzu.codes

Day 30 · ~11 min●

Thirty days ago you rated yourself on six skills. Today you rate yourself again — same questions, same scale. Then you see the gap.

I ran the capstone this morning. One function. Took a list of campaign dicts, grouped by channel, flagged three underperformers. My manager asked how I built it so fast.

That function pulled in everything — f-strings, loops, dict accumulators, sorted key functions, error handling for messy data. Thirty days of small wins composed into one report your team will actually use. Rate yourself on what you can do now, not what you wish you could.

I want to see the delta.

Same six rows. Answer honestly. The chart shows Day 1 and today side by side — and that gap is yours to keep, and to build on.

What you built

30 days, 20 code lessons, 1 real campaign report.

From first variable to full pipeline

Week 1 — Variables, f-strings, booleans, if/elif — storing and printing a single CPL
Week 2 — Lists, for loops, dicts, nested structures — grouping 40 campaigns by channel
Week 3 — Functions, defaults, CSV/JSON parsing — turning a one-off into a reusable weekly script
Week 4 — Comprehensions, sorted key, error handling, capstone — the quarterly report that runs itself

What the chart shows

Column	Meaning
Left bar	Day 1 self-rating
Right bar	Today's rating
Gap	Real, measurable growth

Ahead: automation-for-professionals starts where this track ends. The first thing you meet there is a live Gmail connection — your code fetches real emails from your inbox.

Self-Assessment

0/6

Not at allSlightlyModeratelyVeryExtremely

1.Now I can store typed values in variables and use them in formatted outputs and calculations.

2.Now I can write a reusable function with typed parameters, defaults, and a clean return value.

3.Now I can loop over a list of dicts, accumulate results, and group data by a key field.

4.Now I can build a dict from a list, nest dicts, and retrieve values safely with .get().

5.Now I can parse CSV and JSON text into Python data structures ready for analysis.

6.Now I can use try/except to handle missing keys and bad values without crashing my script.