How much does zuzu.codes cost?

The starter track is free — read all lessons and practice for free. Full access to every track (current and future) is $14.99/month. Cancel anytime.

How long does each track take?

Each track is designed as a 30-day challenge — one lesson per day, about 15 minutes each. Go at your own pace, but the structure is built around daily consistency.

What's the lesson format?

Each lesson is a student-teacher dialogue with code examples, followed by a hands-on code challenge in an in-browser editor. You read, you understand, then you write real code.

Do I need prior coding experience?

Our beginner track starts from absolute zero — no prior experience needed. Advanced tracks build on earlier ones, and the platform tells you exactly where to start.

How is zuzu.codes different from freeCodeCamp or Codecademy?

zuzu.codes uses a structured 30-day track format with dialogue-based teaching, an in-browser code editor, and gamification (XP, streaks, progress tracking). The format builds genuine understanding through daily practice.

Week 4: Data Wrangling and the Investor Update — Python For Entrepreneurs

Day 23 · ~11 min●

You have three weeks of Python tools. This week you wire them into one pipeline. What's the most painful part of your current Monday investor update?

Definitely the delta computation. I compare this week's MRR to last week's manually, round it, check if it's growth or decline. Every time I make a rounding error.

That rounding error is now a bug in code — and you can fix it once, forever. This week you add list comprehensions to filter and rank customers, regex to extract names from messy text, sorting to rank plans by MRR, try/except to handle missing fields gracefully, and finally the full capstone: investor_update that runs the whole pipeline end to end.

So the output is literally the text I'd paste into a Monday email?

Markdown-formatted, ready to paste into Gmail or Notion. Your Monday becomes investor_update(metrics) — one call, one report, zero Stripe tabs.

This week

Day 24 — filter and rank top customers with list comprehensions
Day 25 — extract names from raw text with re.findall + strip() + join()
Day 26 — sort and group plans by total MRR using sorted(key=...)
Day 27 — handle missing keys and zero-division with try/except
Day 28 — capstone: investor_update(metrics) — the full Monday pipeline

Goal: by Friday your investor update runs itself.

Week 4

7 lessons this week

Day 23 · ~11 min●

You have three weeks of Python tools. This week you wire them into one pipeline. What's the most painful part of your current Monday investor update?

Definitely the delta computation. I compare this week's MRR to last week's manually, round it, check if it's growth or decline. Every time I make a rounding error.

So the output is literally the text I'd paste into a Monday email?

Markdown-formatted, ready to paste into Gmail or Notion. Your Monday becomes investor_update(metrics) — one call, one report, zero Stripe tabs.

This week

Day 24 — filter and rank top customers with list comprehensions
Day 25 — extract names from raw text with re.findall + strip() + join()
Day 26 — sort and group plans by total MRR using sorted(key=...)
Day 27 — handle missing keys and zero-division with try/except
Day 28 — capstone: investor_update(metrics) — the full Monday pipeline

Goal: by Friday your investor update runs itself.

This week

Day 24 — filter and rank top customers with list comprehensions

Day 25 — extract names from raw text with re.findall + strip() + join()

Day 26 — sort and group plans by total MRR using sorted(key=...)

Day 27 — handle missing keys and zero-division with try/except

Day 28 — capstone: investor_update(metrics) — the full Monday pipeline

Goal: by Friday your investor update runs itself.

This week

Day 24 — filter and rank top customers with list comprehensions

Day 25 — extract names from raw text with re.findall + strip() + join()

Day 26 — sort and group plans by total MRR using sorted(key=...)

Day 27 — handle missing keys and zero-division with try/except

Day 28 — capstone: investor_update(metrics) — the full Monday pipeline

Goal: by Friday your investor update runs itself.