Python for data analysts

46 levels in 8 chapters

Learn Python and pandas for data analysis: from first variables to groupby, merges and cleaning messy data, run in your browser.

By the end you can load, filter, group, merge and clean real tables in pandas, and explain every step to the person who asked. Every level is open; the path only suggests an order.

Chapter 1 · 5 levels

Python basics

5 lanterns to light

  1. Compute an order total
  2. How many boxes does the warehouse need?
  3. Format a shelf label
  4. Pick the right shipping fee
  5. Boss Boss: build the checkout calculator

Chapter 2 · 6 levels

Lists, dictionaries and loops

6 lanterns to light

  1. Slice a week of orders
  2. Total a cart with a loop
  3. Update the stock sheet
  4. Count orders by channel
  5. Filter products in one line
  6. Boss Boss: revenue by channel, by hand

Chapter 3 · 5 levels

Functions

5 lanterns to light

  1. A line-total function with a discount
  2. Return an order summary
  3. Tag orders by size
  4. Sort products with a key
  5. Boss Boss: parse the supplier export

Chapter 4 · 6 levels

pandas first look

6 lanterns to light

  1. Your first DataFrame
  2. How big is the table?
  3. Pick the columns you need
  4. Series or DataFrame?
  5. Count orders by status
  6. Boss Boss: the first-look report

Chapter 5 · 6 levels

Filtering and sorting

6 lanterns to light

  1. Rows by position, columns by name
  2. Only the marketplace orders
  3. Combine conditions with & and |
  4. The ten most expensive products
  5. Lighting in a price band
  6. Boss Boss: who converts on the website?

Chapter 6 · 6 levels

New columns, groupby and aggregation

6 lanterns to light

  1. Compute line revenue
  2. Average shipping fee per channel
  3. A category summary table
  4. Orders by channel and status
  5. Departments with at least seven people
  6. Boss Boss: find the biggest baskets

Chapter 7 · 6 levels

Merging tables

6 lanterns to light

  1. Add product names to order lines
  2. Customers who never ordered
  3. Revenue by category, completed orders only
  4. Why did shipping revenue double?
  5. Shipping fees on lighting orders
  6. Boss Boss: where do returns come from?

Chapter 8 · 6 levels

Cleaning messy data

6 lanterns to light

  1. Where do customers come from?
  2. Fix the state codes
  3. Find duplicate customers
  4. Which weekday sells most?
  5. What does mean() do with missing costs?
  6. Boss Boss: the clean 2025 report

Megan Collins · Head of Sales

"A customer on the phone is buying two of our table lamps and wants the total before she pays. Can you work it out, shipping included?"

Light the next lantern