Fill only what is justified · Data Playground

The question

Prepare net order amounts under the billing team’s explicit missing-value policy.

df Net amounts

Your inputs

`df`

discount is dollars per entire order line, not per unit. Unknown unit prices cannot be assumed zero.

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 16 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	None	0.5	true
P004	Note-book	Paper	West	4	3.5	1	true
P005	" Notebook "	Paper	East	1	3.5	0	true

View all 16 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	None	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	None	true
P008	Note-book	Paper	West	8	3.5	1	true
P009	" Notebook "	Paper	East	None	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

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

Your task · Fill only what is justified

Prepare net order amounts under the billing team’s explicit missing-value policy.

cleanDataFrameexcluded_countnumber

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.

Words in this task

missing: no value was supplied

Net order linesOutputcleanDataFrame
1. Create clean keeping known unit_price and quantity.
2. A missing discount means no discount: fill it with zero.
3. Add net_amount = quantity × unit_price − discount.
4. Keep other fields and source order.
Unpriceable recordsOutputexcluded_countnumber
1. Store the number of excluded source rows in excluded_count.

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

Does the same missing-value policy make sense for both a price and a discount?

Hint 2 — Tools

dropna(), fillna(), copy() and arithmetic

Revisit: Drop missing rows · Fill missing values · Create a numeric column

Hint 3 — Approach

Remove unpriceable lines, apply the authorised discount policy and compute net amounts.

Explained solution

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

clean = df.dropna(subset=['unit_price', 'quantity']).copy()
clean['discount'] = clean.discount.fillna(0)
clean['net_amount'] = clean.quantity * clean.unit_price - clean.discount
excluded_count = len(df) - len(clean)
clean

View supplied setup

This code is already at the top of the editor.

import pandas as pd

# Supplied input: df (16 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, None, 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, None, True),
        ('P008', 'Note-book', 'Paper', 'West', 8, 3.5, 1, True),
        ('P009', ' Notebook ', 'Paper', 'East', None, 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),
    ],
    columns=['order_id', 'product', 'category', 'region', 'quantity', 'unit_price', 'discount', 'in_stock'],
)

Why these steps exist

A missing discount has an authorised zero meaning. A missing quantity or price has no such meaning.
The discount applies once to the whole line, after quantity times unit price.

Another valid approach

You may fill discounts before or after selecting eligible rows, provided no unknown price is replaced.