Colormaps (sequential, diverging, qualitative) in Matplotlib - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the colormap module from matplotlib.

Matplotlib

from matplotlib import [1]

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

Acm

Bpyplot

Cstyle

Dcolors

Attempts:

3 left

💡 Hint

Common Mistakes

Importing pyplot instead of cm.

Importing colors which is related but not the colormap module.

The cm module in matplotlib contains colormaps used for coloring plots.

2fill in blank

medium

Complete the code to create a sequential colormap called 'Blues'.

Matplotlib

cmap = cm.[1]

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

ASet1

BBlues

Ccoolwarm

DPiYG

Attempts:

3 left

💡 Hint

Common Mistakes

Using diverging colormaps like 'coolwarm' for sequential data.

Using qualitative colormaps like 'Set1' here.

Blues is a sequential colormap that goes from light to dark blue.

3fill in blank

hard

Fix the error in the code to get a diverging colormap named 'PiYG'.

Matplotlib

div_cmap = cm.[1]

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

ABlues

Bviridis

CSet3

DPiYG

Attempts:

3 left

💡 Hint

Common Mistakes

Using sequential colormaps for diverging data.

Using qualitative colormaps like 'Set3' instead.

PiYG is a diverging colormap useful for data with a meaningful midpoint.

4fill in blank

hard

Fill both blanks to create a qualitative colormap named 'Set1' and apply it to a scatter plot.

Matplotlib

scatter = plt.scatter(x, y, c=labels, cmap=cm.[1])
plt.colorbar(scatter, cmap=cm.[2])

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

ASet1

Bcoolwarm

Dviridis

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing different colormaps for scatter and colorbar.

Using sequential or diverging colormaps for categorical data.

Set1 is a qualitative colormap good for categorical data. It is used both in the scatter and colorbar.

5fill in blank

hard

Fill all three blanks to create a dictionary of colormaps for each type and print the name of the sequential colormap.

Matplotlib

colormaps = {
    'sequential': cm.[1],
    'diverging': cm.[2],
    'qualitative': cm.[3]
}
print(colormaps['sequential'].name)

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

ABlues

BPiYG

CSet1

Dviridis

Attempts:

3 left

💡 Hint

Common Mistakes

Assigning colormaps to wrong categories.

Using qualitative colormaps as sequential or diverging.

viridis is a popular sequential colormap, PiYG is diverging, and Set1 is qualitative.

Practice

1. Which type of colormap is best to use when your data has a natural order from low to high values?

easy

A. Sequential colormap

B. Diverging colormap

C. Qualitative colormap

D. Random colormap

Solution

1. Step 1: Understand data order

   Sequential colormaps are designed for data that goes from low to high in a smooth gradient.

2. Step 2: Match colormap type to data

   Diverging is for data with a middle point, qualitative is for categories, so sequential fits ordered data best.

3. Final Answer:

   Sequential colormap -> Option A

4. Quick Check:

   Ordered data = Sequential colormap [OK]

Hint: Ordered data? Pick sequential colormap [OK]

Common Mistakes:

• Choosing diverging for ordered data
• Using qualitative for numeric ranges
• Thinking random colormap exists

2. Which of the following is the correct way to apply the 'coolwarm' diverging colormap to a matplotlib scatter plot?

easy

A. plt.scatter(x, y, c=z, cmap='coolwarm')

B. plt.scatter(x, y, color='coolwarm')

C. plt.scatter(x, y, cmap='coolwarm')

D. plt.scatter(x, y, c=z, color_map='coolwarm')

Solution

1. Step 1: Check scatter plot parameters

   The parameter to set colors by data is c=, and colormap is set by cmap=.

2. Step 2: Identify correct syntax

   plt.scatter(x, y, c=z, cmap='coolwarm') uses both c=z and cmap='coolwarm', which is correct syntax.

3. Final Answer:

   plt.scatter(x, y, c=z, cmap='coolwarm') -> Option A

4. Quick Check:

   Use c= for data colors and cmap= for colormap [OK]

Hint: Use c= for data, cmap= for colormap in plots [OK]

Common Mistakes:

• Using color= instead of c= for data colors
• Using color_map instead of cmap
• Omitting c= when using cmap

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

import matplotlib.pyplot as plt
import numpy as np

values = np.array([1, 2, 3, 4, 5])
plt.scatter(values, values, c=values, cmap='tab10')
plt.show()

medium

A. Sequential colormap

B. Diverging colormap

C. Qualitative colormap

D. No colormap applied

Solution

1. Step 1: Identify the colormap used

   The colormap 'tab10' is a qualitative colormap designed for categorical data.

2. Step 2: Understand the effect on scatter plot

   Even though values are numeric, using tab10 applies distinct colors for categories, so qualitative colormap is used.

3. Final Answer:

   Qualitative colormap -> Option C

4. Quick Check:

   tab10 = Qualitative colormap [OK]

Hint: tab10 means qualitative colormap [OK]

Common Mistakes:

• Assuming tab10 is sequential
• Thinking no colormap applied
• Confusing diverging with qualitative

4. The following code throws an error. What is the mistake?

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-1, 1, 10)
y = x**2
plt.scatter(x, y, c=y, cmap='sequential')
plt.show()

medium

A. The variable y cannot be used for colors

B. x and y arrays have different lengths

C. Missing plt.colorbar() call

D. The colormap name 'sequential' is invalid

Solution

1. Step 1: Check colormap name validity

   Matplotlib does not have a colormap named 'sequential'. It has specific names like 'viridis', 'plasma', etc.

2. Step 2: Confirm other code parts

   Variables x and y have same length, y can be used for colors, and plt.colorbar() is optional.

3. Final Answer:

   The colormap name 'sequential' is invalid -> Option D

4. Quick Check:

   Invalid colormap name causes error [OK]

Hint: Use exact colormap names like 'viridis', not 'sequential' [OK]

Common Mistakes:

• Using generic names like 'sequential' as colormap
• Assuming plt.colorbar() is required
• Thinking y can't be used for colors

5. You want to visualize temperature changes where values below zero are cold, above zero are warm, and zero is neutral. Which colormap type should you choose and why?

hard

A. Sequential, because temperature is ordered from low to high

B. Diverging, because data has a meaningful center at zero

C. Qualitative, because temperature categories are distinct

D. Random, to highlight all values differently

Solution

1. Step 1: Analyze data characteristics

   Temperature data has a natural center point at zero separating cold and warm values.

2. Step 2: Match colormap type to data

   Diverging colormaps highlight differences around a center value, perfect for zero-centered data.

3. Final Answer:

   Diverging, because data has a meaningful center at zero -> Option B

4. Quick Check:

   Center point data = Diverging colormap [OK]

Hint: Center value? Use diverging colormap [OK]

Common Mistakes:

• Choosing sequential ignoring center zero
• Using qualitative for numeric temperature
• Thinking random colormap exists