Dashboard layout patterns in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ visualization

intermediate

Identify the correct subplot layout for a 2x2 dashboard

You want to create a dashboard with 4 charts arranged in 2 rows and 2 columns using matplotlib. Which code snippet correctly creates this layout?

A

fig, axs = plt.subplots(2, 2)
axs[0, 0].plot(data1)
axs[0, 1].plot(data2)
axs[1, 0].plot(data3)
axs[1, 1].plot(data4)

B

fig, axs = plt.subplots(4, 1)
axs[0].plot(data1)
axs[1].plot(data2)
axs[2].plot(data3)
axs[3].plot(data4)

C

fig, axs = plt.subplots(3, 3)
axs[0, 0].plot(data1)
axs[0, 1].plot(data2)
axs[1, 0].plot(data3)
axs[1, 1].plot(data4)

D

fig, axs = plt.subplots(1, 4)
axs[0].plot(data1)
axs[1].plot(data2)
axs[2].plot(data3)
axs[3].plot(data4)

💡 Hint

Think about how many rows and columns you need and how matplotlib returns axes in a 2D array for multiple rows and columns.

Using plt.subplots(2, 2) creates a 2x2 grid of axes. The axes are accessed by axs[row, column]. Other options create different layouts.

🧠 Conceptual

intermediate

Best practice for dashboard layout to improve readability

Which of the following is the best practice to improve readability in a dashboard layout?

A Group related charts together and use consistent spacing

B Place all charts in a single row to avoid scrolling

C Use as many colors as possible to differentiate charts

D Make charts as small as possible to fit more on screen

💡 Hint

Think about how grouping and spacing affect how easily someone can understand the dashboard.

Grouping related charts and using consistent spacing helps users quickly find and compare information. Overcrowding or too many colors can confuse users.

❓ dax_lod_result

advanced

Calculate total sales per region ignoring filters on product category

Given a sales table with columns Region, ProductCategory, and SalesAmount, which DAX measure calculates total sales per region ignoring any filters on ProductCategory?

A Total Sales by Region = CALCULATE(SUM(Sales[SalesAmount]), REMOVEFILTERS(Sales[Region]))

B Total Sales by Region = SUM(Sales[SalesAmount])

C Total Sales by Region = CALCULATE(SUM(Sales[SalesAmount]), ALL(Sales[ProductCategory]))

D Total Sales by Region = CALCULATE(SUM(Sales[SalesAmount]), ALL(Sales[Region]))

💡 Hint

Use a function that removes filters on ProductCategory but keeps filters on Region.

Using ALL(Sales[ProductCategory]) removes filters on ProductCategory only, so the measure sums sales ignoring product category filters but respects region filters.

🔧 Debug

advanced

Identify the error in this matplotlib dashboard layout code

What error will this code produce when creating a 3x1 dashboard layout?

fig, axs = plt.subplots(3, 1)
axs[0, 0].plot(data1)
axs[1, 0].plot(data2)
axs[2, 0].plot(data3)

Matplotlib

fig, axs = plt.subplots(3, 1)
axs[0, 0].plot(data1)
axs[1, 0].plot(data2)
axs[2, 0].plot(data3)

A No error, code runs correctly

B TypeError: 'AxesSubplot' object is not subscriptable

C AttributeError: 'list' object has no attribute 'plot'

D IndexError: index 1 is out of bounds for axis 0 with size 1

💡 Hint

Check how matplotlib returns axes when there is only one column.

When plt.subplots(3, 1) is used, axs is a 1D array of axes, so axs[0, 0] is invalid. You should use axs[0], axs[1], etc.

🎯 Scenario

expert

Design a responsive dashboard layout for different screen sizes

You are designing a dashboard that must look good on both desktop and mobile screens. Which approach best supports responsive layout using matplotlib?

A Create one large figure and rely on zooming to view details on smaller screens

B Use <code>plt.subplots()</code> with fixed rows and columns and resize the figure manually

C Create separate figures for desktop and mobile and switch based on screen size

D Use a flexible grid layout with <code>GridSpec</code> and adjust subplot sizes dynamically

💡 Hint

Think about how to adjust layout dynamically rather than fixed sizes.

GridSpec allows flexible control of subplot sizes and positions, which can be adjusted programmatically to fit different screen sizes, supporting responsive design.