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Week 2: Collections, Loops, and Customer Data — Python For Entrepreneurs

Day 9 · ~11 min●

Last week you formatted a single MRR value. Your Stripe export has 500 rows. What's your current workflow for grouping those customers by plan?

Export to CSV, open in Excel, pivot table, manually check the totals. It takes 20 minutes every Monday.

This week Python replaces that 20 minutes. A list is your cap table — ordered rows, each one a customer. A dict is a customer record — fields you look up by name, not column position. And a for loop is Python scanning every row in your Stripe export, except it does it in milliseconds.

So by the end of this week I can group 500 customers by plan in a single function call?

Exactly. Five functions this week. Day 10 filters active customers above a minimum MRR. Day 11 computes ARPU per customer with a for loop. Day 12 finds the first churned customer on a given plan. Day 13 groups all customers by plan into a dict. Day 14 produces a full plan summary with totals and averages. The pivot table is now three lines of Python.

This week

Day 10 — filter a list of customer dicts by mrr and status with append and in
Day 11 — iterate a list with for and enumerate(), compute ARPU per customer
Day 12 — find the first churned customer on a plan using while + break
Day 13 — group customers by plan into a dict with .get(), .keys(), .items()
Day 14 — produce a nested plan summary with total MRR, seats, and avg ARPU

Goal: by Friday you can replace your Monday Stripe pivot with one function call.

Week 2

7 lessons this week

Day 9 · ~11 min●

Last week you formatted a single MRR value. Your Stripe export has 500 rows. What's your current workflow for grouping those customers by plan?

Export to CSV, open in Excel, pivot table, manually check the totals. It takes 20 minutes every Monday.

So by the end of this week I can group 500 customers by plan in a single function call?

This week

Day 10 — filter a list of customer dicts by mrr and status with append and in
Day 11 — iterate a list with for and enumerate(), compute ARPU per customer
Day 12 — find the first churned customer on a plan using while + break
Day 13 — group customers by plan into a dict with .get(), .keys(), .items()
Day 14 — produce a nested plan summary with total MRR, seats, and avg ARPU

Goal: by Friday you can replace your Monday Stripe pivot with one function call.

This week

Day 10 — filter a list of customer dicts by mrr and status with append and in

Day 11 — iterate a list with for and enumerate(), compute ARPU per customer

Day 12 — find the first churned customer on a plan using while + break

Day 13 — group customers by plan into a dict with .get(), .keys(), .items()

Day 14 — produce a nested plan summary with total MRR, seats, and avg ARPU

Goal: by Friday you can replace your Monday Stripe pivot with one function call.

This week

Day 10 — filter a list of customer dicts by mrr and status with append and in

Day 11 — iterate a list with for and enumerate(), compute ARPU per customer

Day 12 — find the first churned customer on a plan using while + break

Day 13 — group customers by plan into a dict with .get(), .keys(), .items()

Day 14 — produce a nested plan summary with total MRR, seats, and avg ARPU

Goal: by Friday you can replace your Monday Stripe pivot with one function call.