Module 4: Multi-Step Analysis•25 min

Chaining Analysis Steps

Progress Tracking

Each variable is a checkpoint you can inspect. When something’s wrong, print any intermediate result to find the problem.

Name Your Variables Well

customer_totals tells you what’s in it. temp1 tells you nothing. Good variable names are documentation.

Adding Context with Merge

After computing your aggregation, you often need to merge the result back with another DataFrame to add names, labels, or other context.

Table: orders

id	cust_id	order_date	order_details	total_order_cost
1	3	2019-03-04	Coat	100
2	3	2019-03-01	Shoes	80
3	3	2019-03-07	Skirt	30
4	7	2019-02-01	Coat	25
5	7	2019-03-10	Shoes	80

Table: customers

id	first_name	last_name	city	phone_number
8	John	Joseph	San Francisco	928-386-8164
7	Jill	Michael	Austin	813-297-0692
4	William	Daniel	Denver	813-368-1200
5	Henry	Jackson	Miami	808-601-7513
13	Emma	Isaac	Miami	808-690-5201

Tables: orders, customers

Ratios and Percentages

Percentage calculations follow a template: compute the per-group number, the total, then divide.

Table: employee

id	first_name	last_name	age	sex	employee_title	department	salary	target	bonus	email	city	address	manager_id
5	Max	George	26	M	Sales	Sales	1300	200	150	Max@company.com	California	2638 Richards Avenue	1
13	Katty	Bond	56	F	Manager	Management	150000	0	300	Katty@company.com	Arizona		1
11	Richerd	Gear	57	M	Manager	Management	250000	0	300	Richerd@company.com	Alabama		1
10	Jennifer	Dion	34	F	Sales	Sales	1000	200	150	Jennifer@company.com	Alabama		13
19	George	Joe	50	M	Manager	Management	100000	0	300	George@company.com	Florida	1003 Wyatt Street	1

Tables: employee

Customers with Large Orders

Table: online_store_customers

customer_id	customer_name
1	Alice Johnson
2	Bob Smith
3	Carol Williams
4	David Brown
5	Emma Davis

Table: online_store_orders

order_id	customer_id	amount	status
101	1	150	paid
102	1	200	paid
103	1	75	paid
104	2	250	paid
105	3	180	paid

Tables: online_store_customers, online_store_orders

Employee and Manager Salaries

Table: employee

id	first_name	last_name	age	sex	employee_title	department	salary	target	bonus	email	city	address	manager_id
5	Max	George	26	M	Sales	Sales	1300	200	150	Max@company.com	California	2638 Richards Avenue	1
13	Katty	Bond	56	F	Manager	Management	150000	0	300	Katty@company.com	Arizona		1
11	Richerd	Gear	57	M	Manager	Management	250000	0	300	Richerd@company.com	Alabama		1
10	Jennifer	Dion	34	F	Sales	Sales	1000	200	150	Jennifer@company.com	Alabama		13
19	George	Joe	50	M	Manager	Management	100000	0	300	George@company.com	Florida	1003 Wyatt Street	1

Tables: employee

Key Takeaways

Name each step as a variable — this is pandas’ equivalent of CTEs.
Each variable is a checkpoint you can inspect for debugging.
Chain: filter → group → aggregate → merge → filter again.
For ratios: compute numerator and denominator separately, then divide.

What’s Next

The final lesson covers common patterns you’ll use regularly: safe division, replacing slow loops, and knowing when a one-liner is better than multiple steps.

Next upContinue →

Patterns for Common Problems

35 min