Discrete colorbars in Matplotlib - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a discrete colorbar with 5 colors.

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

cmap = plt.cm.get_cmap('viridis', [1])
fig, ax = plt.subplots()
fig.colorbar(plt.cm.ScalarMappable(cmap=cmap), ax=ax, ticks=range(6))
plt.show()

Drag options to blanks, or click blank then click option'

A5

B10

C3

D7

Attempts:

3 left

💡 Hint

Common Mistakes

Using a number that is too large or too small for the discrete colors.

Forgetting to pass the number of colors to get_cmap.

The get_cmap function's second argument sets the number of discrete colors. Here, 5 creates 5 discrete colors.

2fill in blank

medium

Complete the code to set the colorbar ticks correctly for 4 discrete colors.

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

cmap = plt.cm.get_cmap('plasma', 4)
fig, ax = plt.subplots()
cb = fig.colorbar(plt.cm.ScalarMappable(cmap=cmap), ax=ax, ticks=[1])
plt.show()

Drag options to blanks, or click blank then click option'

A[0, 1, 2, 3, 4]

B[1, 2, 3, 4]

C[0.25, 0.5, 0.75, 1]

D[0, 1, 2, 3]

Attempts:

3 left

💡 Hint

Common Mistakes

Using only 4 ticks for 4 colors, which misses the last boundary.

Using fractional ticks that don't align with color boundaries.

The ticks should cover all boundaries of the 4 discrete colors, so 5 ticks from 0 to 4 are needed.

3fill in blank

hard

Fix the error in the code to create a discrete colorbar with 3 colors.

Matplotlib

import matplotlib.pyplot as plt

cmap = plt.cm.get_cmap('coolwarm', 3)
fig, ax = plt.subplots()
cb = fig.colorbar(plt.cm.ScalarMappable(cmap=cmap), ax=ax, ticks=range([1]))
plt.show()

Drag options to blanks, or click blank then click option'

A2

B3

C4

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using ticks range(3) which only gives 3 ticks for 3 colors.

Not setting the number of colors in get_cmap.

To create 3 discrete colors, the colormap must be created with 3 colors. The ticks should be from 0 to 3 to cover boundaries.

4fill in blank

hard

Fill both blanks to create a discrete colorbar with 6 colors and ticks from 0 to 6.

Matplotlib

import matplotlib.pyplot as plt

cmap = plt.cm.get_cmap('[1]', [2])
fig, ax = plt.subplots()
fig.colorbar(plt.cm.ScalarMappable(cmap=cmap), ax=ax, ticks=range(7))
plt.show()

Drag options to blanks, or click blank then click option'

Atab10

B5

C6

Dviridis

Attempts:

3 left

💡 Hint

Common Mistakes

Using a continuous colormap name without specifying discrete colors.

Mismatch between number of colors and ticks.

The colormap name is 'tab10' which supports discrete colors well. The number of colors is 6 to match the ticks from 0 to 6.

5fill in blank

hard

Fill all three blanks to create a discrete colorbar with 4 colors, ticks from 0 to 4, and label the ticks with names.

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

labels = ['Low', 'Medium', 'High', 'Very High']
cmap = plt.cm.get_cmap('[1]', [2])
fig, ax = plt.subplots()
cb = fig.colorbar(plt.cm.ScalarMappable(cmap=cmap), ax=ax, ticks=range([3]))
cb.ax.set_yticklabels(labels)
plt.show()

Drag options to blanks, or click blank then click option'

Acoolwarm

B4

C5

Dviridis

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong number of ticks for the number of colors.

Not matching labels count with colors.

The 'viridis' colormap is used with 4 discrete colors. Ticks go from 0 to 4 to cover boundaries for 4 colors. The labels correspond to the 4 color segments.

Practice

1. What is the main purpose of using a discrete colorbar in a matplotlib plot?

easy

A. To increase the size of the plot figure

B. To create a continuous gradient of colors for smooth data

C. To add labels to the x-axis of a plot

D. To show data with distinct color steps for clear category separation

Solution

1. Step 1: Understand colorbar types

   Continuous colorbars show smooth color changes, while discrete colorbars use fixed color steps.

2. Step 2: Identify the purpose of discrete colorbars

   Discrete colorbars help separate data into clear categories using distinct colors.

3. Final Answer:

   To show data with distinct color steps for clear category separation -> Option D

4. Quick Check:

   Discrete colorbar = distinct color steps [OK]

Hint: Discrete colorbars separate categories with fixed colors [OK]

Common Mistakes:

• Confusing discrete with continuous colorbars
• Thinking colorbars change plot size
• Mixing colorbar purpose with axis labeling

2. Which of the following code snippets correctly creates a discrete colorbar with 3 color steps using matplotlib?

easy

A. cmap = ListedColormap(['red', 'green', 'blue'])
norm = Normalize(0, 3)

B. cmap = LinearSegmentedColormap(['red', 'green', 'blue'])
norm = Normalize(0, 3)

C. cmap = ListedColormap(['red', 'green', 'blue'])
norm = BoundaryNorm([0, 1, 2, 3], cmap.N)

D. cmap = LinearSegmentedColormap(['red', 'green', 'blue'])
norm = BoundaryNorm([0, 1, 2, 3], cmap.N)

Solution

1. Step 1: Identify correct colormap for discrete colors

   ListedColormap is used for discrete colors, LinearSegmentedColormap is for continuous gradients.

2. Step 2: Check normalization method

   BoundaryNorm with boundaries defines discrete steps; Normalize is for continuous scaling.

3. Final Answer:

   cmap = ListedColormap(['red', 'green', 'blue'])\nnorm = BoundaryNorm([0, 1, 2, 3], cmap.N) -> Option C

4. Quick Check:

   ListedColormap + BoundaryNorm = discrete colorbar [OK]

Hint: Use ListedColormap + BoundaryNorm for discrete colorbars [OK]

Common Mistakes:

• Using Normalize instead of BoundaryNorm
• Using LinearSegmentedColormap for discrete colors
• Incorrect boundary list length

3. What will be the output color for a value of 1.5 in this discrete colorbar setup?

import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap, BoundaryNorm

cmap = ListedColormap(['yellow', 'orange', 'red'])
norm = BoundaryNorm([0, 1, 2, 3], cmap.N)
color = cmap(norm(1.5))
print(color)

medium

A. (1.0, 1.0, 0.0, 1.0) # yellow color

B. (1.0, 0.647, 0.0, 1.0) # orange color

C. (1.0, 0.0, 0.0, 1.0) # red color

D. (0.5, 0.5, 0.5, 1.0) # gray color

Solution

1. Step 1: Understand BoundaryNorm mapping

   BoundaryNorm maps values 0-1 to first color (yellow), 1-2 to second (orange), 2-3 to third (red).

2. Step 2: Find color for value 1.5

   1.5 lies between 1 and 2, so it maps to the second color: orange (RGB approx (1.0, 0.647, 0.0, 1.0)).

3. Final Answer:

   (1.0, 0.647, 0.0, 1.0) # orange color -> Option B

4. Quick Check:

   Value 1.5 in 1-2 range = orange [OK]

Hint: Check which boundary interval the value falls into [OK]

Common Mistakes:

• Choosing first or third color incorrectly
• Confusing Normalize with BoundaryNorm behavior
• Misreading boundary intervals

4. Identify the error in this code that tries to create a discrete colorbar with 4 colors:

import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap, BoundaryNorm

cmap = ListedColormap(['blue', 'green', 'yellow', 'red'])
norm = BoundaryNorm([0, 1, 2, 3], cmap.N)
plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap))
plt.show()

medium

A. Boundary list length should be one more than number of colors

B. ListedColormap cannot have more than 3 colors

C. plt.colorbar requires a continuous colormap

D. ScalarMappable is not compatible with BoundaryNorm

Solution

1. Step 1: Check boundary list length

   BoundaryNorm boundaries must have length = number of colors + 1 to define intervals correctly.

2. Step 2: Compare boundaries and colors

   Here, 4 colors but only 4 boundaries given; should be 5 boundaries like [0,1,2,3,4].

3. Final Answer:

   Boundary list length should be one more than number of colors -> Option A

4. Quick Check:

   Boundaries = colors + 1 [OK]

Hint: Boundaries count = colors count + 1 for discrete colorbars [OK]

Common Mistakes:

• Using equal boundaries and colors length
• Thinking ScalarMappable is incompatible
• Believing ListedColormap limits colors

5. You have data with 5 categories labeled 0 to 4. You want a discrete colorbar with distinct colors for each category and labels shown on the colorbar ticks. Which approach correctly achieves this in matplotlib?

hard

A. Use ListedColormap with 5 colors, BoundaryNorm with boundaries [0,1,2,3,4,5], and set colorbar ticks at [0.5,1.5,2.5,3.5,4.5] with labels ['0','1','2','3','4']

B. Use LinearSegmentedColormap with 5 colors and Normalize from 0 to 4, then set colorbar ticks at [0,1,2,3,4]

C. Use ListedColormap with 4 colors, BoundaryNorm with boundaries [0,1,2,3,4], and set colorbar ticks at [0,1,2,3]

D. Use ListedColormap with 5 colors, Normalize from 0 to 5, and set colorbar ticks at [0,1,2,3,4]

Solution

1. Step 1: Set colormap and boundaries for 5 categories

   ListedColormap with 5 colors matches 5 categories; BoundaryNorm boundaries must be length 6 for 5 intervals.

2. Step 2: Position ticks between boundaries

   Ticks at midpoints like 0.5,1.5,... label categories clearly on discrete colorbar.

3. Final Answer:

   Use ListedColormap with 5 colors, BoundaryNorm with boundaries [0,1,2,3,4,5], and set colorbar ticks at [0.5,1.5,2.5,3.5,4.5] with labels ['0','1','2','3','4'] -> Option A

4. Quick Check:

   Boundaries + mid-ticks label discrete categories [OK]

Hint: Use boundaries + midpoints for ticks to label categories [OK]

Common Mistakes:

• Using continuous colormap for discrete data
• Setting ticks at boundaries instead of midpoints
• Mismatching number of colors and boundaries