Where do categories overlap? · Data Playground

The question

Compare product categories across regions using available stock only.

df Category matrix

Your inputs

`df`

One row is an order line. Count lines regardless of their quantity.

Field meanings and units

order_id: Order or order-line identifier, as defined by this brief.
product: Recorded product label.
category: Product category.
region: Sales region.
quantity: Number of units.
unit_price: Dollars per unit.
discount: Discount in dollars for the whole order line.
in_stock: Whether the order line is marked available.

`df` · first 5 of 18 rows
`order_id`	`product`	`category`	`region`	`quantity`	`unit_price`	`discount`	`in_stock`
P001	" Notebook "	Paper	East	2	3.5	0	false
P002	PEN	Writing	West	5	1.2	None	true
P003	" pencil"	Writing	East	3	0.8	0.5	true
P004	Note-book	Paper	West	4	3.5	1	true
P005	" Notebook "	Paper	East	1	3.5	0	true

View all 18 rows

`df` · complete input
`order_id`	`product`	`category`	`region`	`quantity`	`unit_price`	`discount`	`in_stock`
P001	" Notebook "	Paper	East	2	3.5	0	false
P002	PEN	Writing	West	5	1.2	None	true
P003	" pencil"	Writing	East	3	0.8	0.5	true
P004	Note-book	Paper	West	4	3.5	1	true
P005	" Notebook "	Paper	East	1	3.5	0	true
P006	PEN	Writing	West	6	1.2	None	false
P007	" pencil"	Writing	East	2	0.8	0.5	true
P008	Note-book	Paper	West	8	3.5	1	true
P009	" Notebook "	Paper	East	2	3.5	0	true
P010	PEN	Writing	West	5	1.2	None	true
P011	" pencil"	Writing	East	3	0.8	0.5	false
P012	Note-book	Paper	West	4	3.5	1	true
P013	" Notebook "	Paper	East	1	3.5	0	true
P014	PEN	Writing	West	6	1.2	None	true
P015	" pencil"	Writing	East	2	0.8	0.5	true
P016	Note-book	Paper	West	8	3.5	1	false
P017	" Notebook "	Paper	East	2	3.5	0	true
P018	PEN	Writing	West	5	1.2	None	true

The editable setup in Your Python creates these inputs when you run it.

Your task · Where do categories overlap?

Compare product categories across regions using available stock only.

cross_tabDataFramecategoriesnumber

Deliverables

Make the outputs listed below. Use each name exactly as shown. Then choose Check answer.

Python name key

Teal outlines name table columns. Violet left rules name Python objects. Dotted rules mark syntax, functions and parameters. Use each name exactly as shown.

Category by region countsOutputcross_tabDataFrame
1. Create cross_tab with one row per category and one column per region for available (in_stock=True) records.
2. Each cell is the number of records in that category and region, not the sum of quantity.
Observed categoriesOutputcategoriesnumber
1. Store the number of distinct categories in the in_stock=True rows in categories.

Data policy Keep all supplied source tables unchanged. Work on separate outputs.

Help Hints & explained solution

Choose the amount of help you need. Opening one hint does not reveal the others.

Hint 1 — Think

Is the requested cell a count of records or a sum of quantities?

Hint 2 — Tools

Boolean selection, pd.crosstab() for category-by-region counts, and nunique()

Revisit: Meet a DataFrame · Summarise groups · What values exist? · Combine conditions · Answer one numerical question

Hint 3 — Approach

Select available records, count category and region combinations, then count distinct available categories.

Explained solution

Add these lines below # Your work in the editor. The supplied input setup is already there.

available = df[df.in_stock]
cross_tab = pd.crosstab(available['category'], available['region'])
categories = available['category'].nunique()
cross_tab

View supplied setup

This code is already at the top of the editor.

import pandas as pd

# Supplied input: df (18 rows)
df = pd.DataFrame(
    [
        ('P001', ' Notebook ', 'Paper', 'East', 2, 3.5, 0, False),
        ('P002', 'PEN', 'Writing', 'West', 5, 1.2, None, True),
        ('P003', ' pencil', 'Writing', 'East', 3, 0.8, 0.5, True),
        ('P004', 'Note-book', 'Paper', 'West', 4, 3.5, 1, True),
        ('P005', ' Notebook ', 'Paper', 'East', 1, 3.5, 0, True),
        ('P006', 'PEN', 'Writing', 'West', 6, 1.2, None, False),
        ('P007', ' pencil', 'Writing', 'East', 2, 0.8, 0.5, True),
        ('P008', 'Note-book', 'Paper', 'West', 8, 3.5, 1, True),
        ('P009', ' Notebook ', 'Paper', 'East', 2, 3.5, 0, True),
        ('P010', 'PEN', 'Writing', 'West', 5, 1.2, None, True),
        ('P011', ' pencil', 'Writing', 'East', 3, 0.8, 0.5, False),
        ('P012', 'Note-book', 'Paper', 'West', 4, 3.5, 1, True),
        ('P013', ' Notebook ', 'Paper', 'East', 1, 3.5, 0, True),
        ('P014', 'PEN', 'Writing', 'West', 6, 1.2, None, True),
        ('P015', ' pencil', 'Writing', 'East', 2, 0.8, 0.5, True),
        ('P016', 'Note-book', 'Paper', 'West', 8, 3.5, 1, False),
        ('P017', ' Notebook ', 'Paper', 'East', 2, 3.5, 0, True),
        ('P018', 'PEN', 'Writing', 'West', 5, 1.2, None, True),
    ],
    columns=['order_id', 'product', 'category', 'region', 'quantity', 'unit_price', 'discount', 'in_stock'],
)

Why these steps exist

The cells count order lines, not quantities, so large orders do not weigh more than small ones.
The category and region labels identify each count; their alphabetical order does not affect the result.

Another valid approach

Grouping by category and region and then reshaping the counts is also valid. Keep the category and region labels attached to their values.