Prepare a shareable figure · Data Playground

The question

Prepare a clear, exportable comparison of units sold by product category.

df Shareable figure

Your inputs

`df`

One row is one recorded sale line. quantity is the units already sold, irrespective of current in_stock availability; unit_price is not additive.

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 20 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 20 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
P019	" pencil"	Writing	East	3	0.8	0.5	true
P020	Note-book	Paper	West	4	3.5	1	true

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

Your task · Prepare a shareable figure

Prepare a clear, exportable comparison of units sold by product category.

unitsSeriesfigFigurechallenge.pngPNG file

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 unit totalsOutputunitsSeries
1. Store the total quantity sold for each category in units.
FigureOutputfigFigure
1. Create fig comparing category unit totals.
2. Choose labelled bars or individual points; either orientation is valid.
3. Bars must start at zero.
4. Add a meaningful title and measure labels
5. Display the figure.
Identify Category and Units on the appropriate axes; extra wording is welcome.
PNG exportOutputchallenge.pngPNG file
1. Save the finished fig as challenge.png at 150 dpi, including its labels.

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 measure can be added meaningfully across order lines?

Hint 2 — Tools

groupby(), sum(), Axes.bar() or Axes.barh(), and Figure.savefig(), or Axes.scatter() for a labelled point comparison

Revisit: Summarise groups · Exact precomputed bars · Save a figure · Avoid misleading charts · Scatter plot

Hint 3 — Approach

Total units by category, create and label the comparison, then export the same finished figure.

Explained solution

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

units = df.groupby('category').quantity.sum()
fig, ax = plt.subplots()
ax.bar(units.index, units.values)
ax.set(title='Units sold by category', xlabel='Category', ylabel='Units')
fig.tight_layout()
fig.savefig('challenge.png', dpi=150, bbox_inches='tight')
plt.show()

View supplied setup

This code is already at the top of the editor.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Supplied input: df (20 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),
        ('P019', ' pencil', 'Writing', 'East', 3, 0.8, 0.5, True),
        ('P020', 'Note-book', 'Paper', 'West', 4, 3.5, 1, True),
    ],
    columns=['order_id', 'product', 'category', 'region', 'quantity', 'unit_price', 'discount', 'in_stock'],
)

Why these steps exist

Units are additive across sale lines; neither the number of rows nor the sum of unit prices answers the question.
Exporting after the figure is labelled ensures the saved evidence matches the finished view.

Another valid approach

Labelled individual points are also valid for comparing these summaries. Vertical and horizontal orientations are accepted; bars must use a zero baseline.