Calculate a useful total · Data Playground

The question

Prepare usable sales lines and their monetary totals.

df Sales totals

Your inputs

`df`

quantity is units; unit_price is dollars per unit. All valid quantities and prices are nonnegative.

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	0.8	0.5	true
P004	Note-book	Paper	West	bad	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	0.8	0.5	true
P004	Note-book	Paper	West	bad	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	None	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

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

Your task · Calculate a useful total

Prepare usable sales lines and their monetary totals.

cleanDataFrametotal_salesnumber

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

eligible: rows that meet this task’s stated conditions

Priced order linesOutputcleanDataFrame
1. Create clean by parsing quantity and unit_price numerically, excluding rows where either is unknown.
2. Add line_total = quantity × unit_price.
3. Keep other columns and source order.
Total salesOutputtotal_salesnumber
1. Store the sum of eligible line_total values in total_sales.

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

Which quantities are additive money amounts, and which are unit prices?

Hint 2 — Tools

to_numeric(), dropna(), arithmetic and sum()

Revisit: Convert numeric text · Drop missing rows · Create a numeric column

Hint 3 — Approach

Parse the two required measures, retain complete lines, calculate each line amount and total those amounts.

Explained solution

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

clean = df.copy()
clean['quantity'] = pd.to_numeric(clean.quantity, errors='coerce')
clean['unit_price'] = pd.to_numeric(clean.unit_price, errors='coerce')
clean = clean.dropna(subset=['quantity', 'unit_price']).copy()
clean['line_total'] = clean.quantity * clean.unit_price
total_sales = clean.line_total.sum()
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', '0.8', 0.5, True),
        ('P004', 'Note-book', 'Paper', 'West', 'bad', '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', None, 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),
    ],
    columns=['order_id', 'product', 'category', 'region', 'quantity', 'unit_price', 'discount', 'in_stock'],
)

Why these steps exist

Quantity and unit price must both be numeric before multiplication is meaningful.
Line amounts are additive dollars; summing unit prices would not measure sales.

Another valid approach

Direct column arithmetic and assign-based construction both produce the same line totals.