Data notebook

Works like Jupyter. Answer a manager's questions about a made-up company's data in Python: load the tables, leave the refunds out, group and add up, and store each answer where the notebook can check it.

Notebook

Maple & Main Outfitters0 of 5 answered

QuickCruit Notebooks

maple_main_outfitters_analysis.ipynb

Numbers for Thursday's review
The tables are loaded in tables. Shift and Enter runs a cell, and its last line shows below it.
In [ ]:
```
orders = tables["orders"]
orders.head()
 
```
orders = tables["orders"] orders.head()Shift and Enter runs the cell and moves to the next. Tab indents. To move focus out of the cell, press Escape, then Tab.
In [ ]:
```
refunds = tables["refunds"]
refunds.head()
 
```
refunds = tables["refunds"] refunds.head()Shift and Enter runs the cell and moves to the next. Tab indents. To move focus out of the cell, press Escape, then Tab.
In [ ]:
```
 
```
Start coding or with AI.
Shift and Enter runs the cell and moves to the next. Tab indents. To move focus out of the cell, press Escape, then Tab.

Python 3 | Idle

maple_main_outfitters_analysis.ipynb

Welcome aboard. Before Thursday's review I need a few numbers on the orders at Maple & Main Outfitters from March through May, and I'd like them in a notebook so we can rerun it next quarter.

The orders table has one row per order: order_date, region, product, units and revenue.

Refunds live in their own table, refunds: one row per refunded order, matched by order_id.

Every number leaves refunds out unless I say otherwise. Store each answer in the variable its question names, so I can rerun the notebook and check them all at once.

Maple & Main Outfitters is a made-up company, and its data is made up too. A practice scenario, built for you.

Tables

tables["orders"]484 rows
order_id, order_date, region, product, units, revenue
One row per order: when, where it shipped, what and how many.
tables["refunds"]47 rows
order_id, refund_date
One row per refunded order: its order_id and the day the refund went out.

Questions

Not checked yet

South's revenue
How much revenue did South bring in, with refunds left out? Store it in south_revenue, to the cent.
Or type it here
Top product by units
Which product sold the most units, refunds left out? Store its name in top_product.
Or type it here
April's average order value
What was our average order value in April, refunds left out? Store it in april_aov, to the cent.
Or type it here
The refund rate that stands out
Which region's refund rate stands out? That's refunds out of all its orders. Store the region in refund_region.
Or type it here
Orders of 5 or more units
How many orders had 5 or more units, refunds left out? Store the count in big_orders.
Or type it here

Checking restarts the notebook, runs every cell from the top, then reads each variable and box. Check as often as you like.

Data notebook

Numbers for Thursday's review

Numbers for Thursday's review