Colormaps (sequential, diverging, qualitative) in Matplotlib

Introduction

Colormaps help us show data with colors. They make patterns easy to see.

When showing data that goes from low to high, like temperature changes.

When comparing two extremes, like profit and loss.

When showing categories, like types of fruits or animals.

When making charts easier to understand with colors.

When you want to highlight differences in data clearly.

Syntax

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

# Use a colormap in a plot
plt.imshow(data, cmap='colormap_name')
plt.colorbar()
plt.show()

Replace 'colormap_name' with the name of the colormap you want.

Colormaps are grouped as sequential, diverging, or qualitative.

Examples

Uses a sequential colormap good for data that goes from low to high.

Matplotlib

plt.imshow(data, cmap='viridis')

Uses a diverging colormap to show two extremes with a middle point.

Matplotlib

plt.imshow(data, cmap='coolwarm')

Uses a qualitative colormap for different categories or groups.

Matplotlib

plt.imshow(data, cmap='tab10')

Sample Program

This code shows three plots side by side. Each uses a different colormap type to help see data in different ways.

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

# Create example data
np.random.seed(0)
data = np.random.rand(10, 10)

# Plot with sequential colormap
plt.subplot(1, 3, 1)
plt.title('Sequential (viridis)')
plt.imshow(data, cmap='viridis')
plt.colorbar()

# Plot with diverging colormap
plt.subplot(1, 3, 2)
plt.title('Diverging (coolwarm)')
plt.imshow(data - 0.5, cmap='coolwarm')  # Center around zero
plt.colorbar()

# Plot with qualitative colormap
categories = np.random.randint(0, 10, (10, 10))
plt.subplot(1, 3, 3)
plt.title('Qualitative (tab10)')
plt.imshow(categories, cmap='tab10')
plt.colorbar(ticks=range(10))

plt.tight_layout()
plt.show()

Important Notes

Sequential colormaps are good for ordered data that goes from low to high.

Diverging colormaps highlight differences around a middle value, like zero.

Qualitative colormaps are best for categories without order.

Summary

Colormaps use colors to show data patterns clearly.

Use sequential for data that changes from low to high.

Use diverging to show two opposite extremes.

Use qualitative for different groups or categories.

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