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.

Practice

1. What is the main purpose of using dashboard layout patterns in matplotlib?

easy

A. To organize multiple charts clearly for easy understanding

B. To change the color of charts automatically

C. To add animations to charts

D. To export charts as PDF files

Solution

1. Step 1: Understand dashboard layout purpose

   Dashboard layouts help arrange multiple charts so viewers can understand data easily.

2. Step 2: Identify the correct purpose in options

   Only To organize multiple charts clearly for easy understanding mentions organizing charts clearly, which matches the purpose.

3. Final Answer:

   To organize multiple charts clearly for easy understanding -> Option A

4. Quick Check:

   Dashboard layout = organize charts clearly [OK]

Hint: Dashboards arrange charts clearly for easy reading [OK]

Common Mistakes:

• Confusing layout with color or animation features
• Thinking layout changes export formats
• Assuming layout adds interactivity automatically

2. Which of the following is the correct way to create a 2x2 grid of charts using matplotlib?

easy

A. plt.figure(2, 2)

B. plt.grid(2, 2)

C. plt.subplots(2, 2)

D. plt.plot(2, 2)

Solution

1. Step 1: Recall the function for grid layout

   plt.subplots() creates a grid of subplots; parameters define rows and columns.

2. Step 2: Match correct syntax

   plt.subplots(2, 2) creates a 2 by 2 grid; other options do not create grids.

3. Final Answer:

   plt.subplots(2, 2) -> Option C

4. Quick Check:

   Grid layout = plt.subplots(rows, cols) [OK]

Hint: Use plt.subplots(rows, cols) for grid layouts [OK]

Common Mistakes:

• Using plt.grid() which controls gridlines, not layout
• Confusing plt.figure() with subplot grid creation
• Using plt.plot() which draws single charts only

3. What will be the output layout when running this code?

fig, axs = plt.subplots(1, 3)
for ax in axs:
    ax.plot([1, 2, 3], [1, 4, 9])
plt.tight_layout()
plt.show()

medium

A. Three rows with one chart each stacked vertically

B. A single row with three side-by-side line charts

C. One chart only with three lines overlapping

D. An error because plt.tight_layout() is missing parameters

Solution

1. Step 1: Analyze plt.subplots(1, 3)

   This creates 1 row and 3 columns, so three charts side by side.

2. Step 2: Understand the loop plotting

   Each axis plots the same line chart, so three separate charts appear horizontally.

3. Final Answer:

   A single row with three side-by-side line charts -> Option B

4. Quick Check:

   1 row, 3 cols = 3 charts side by side [OK]

Hint: Rows x cols in plt.subplots defines chart grid shape [OK]

Common Mistakes:

• Thinking 1,3 means 3 rows stacked vertically
• Assuming all lines plot on one chart
• Believing plt.tight_layout() causes errors without args

4. Identify the error in this code snippet for creating a 2x2 dashboard layout:

fig, axs = plt.subplots(2, 2)
axs.plot([1, 2, 3], [3, 2, 1])
plt.show()

medium

A. plt.subplots() cannot create 2x2 grids

B. The plot data lists have different lengths

C. plt.show() is missing parentheses

D. axs is an array; calling axs.plot() causes an error

Solution

1. Step 1: Understand axs type from plt.subplots(2, 2)

   axs is a 2x2 array of axes, not a single axis object.

2. Step 2: Identify incorrect method call

   Calling axs.plot() tries to call plot on the array, which causes an error; must call plot on individual axes.

3. Final Answer:

   axs is an array; calling axs.plot() causes an error -> Option D

4. Quick Check:

   Array of axes needs individual plot calls [OK]

Hint: Call plot on each axis, not on the axes array [OK]

Common Mistakes:

• Calling plot on the whole axs array instead of elements
• Thinking plt.subplots can't create 2x2 grids
• Forgetting plt.show() needs parentheses

5. You want to create a dashboard with 3 charts: one large chart on the left and two smaller stacked charts on the right. Which matplotlib layout pattern best fits this requirement?

hard

A. Use GridSpec to create a 2-column layout with different row spans

B. Use plt.subplots(3, 1) for three stacked charts vertically

C. Use plt.subplots(1, 3) for three charts side by side equally sized

D. Use plt.subplot() three times with default sizes

Solution

1. Step 1: Understand layout needs

   One large chart on left and two smaller stacked on right means uneven grid with row spans.

2. Step 2: Identify suitable layout tool

   GridSpec allows flexible grid with different row/column spans, perfect for this layout.

3. Step 3: Eliminate other options

   plt.subplots(3,1) stacks vertically; plt.subplots(1,3) makes equal columns; plt.subplot() default sizes lack control.

4. Final Answer:

   Use GridSpec to create a 2-column layout with different row spans -> Option A

5. Quick Check:

   Complex layouts need GridSpec flexibility [OK]

Hint: Use GridSpec for uneven dashboard layouts [OK]

Common Mistakes:

• Using plt.subplots with equal-sized grids only
• Stacking all charts vertically when layout differs
• Using plt.subplot() without size control