Colormaps (sequential, diverging, qualitative) in Matplotlib - Mini Project: Build & Apply

Exploring Colormaps with Matplotlib

📖 Scenario: You are working with data visualization to make charts easier to understand. Colors help show differences and patterns in data. Matplotlib has special color sets called colormaps. These colormaps come in three types: sequential, diverging, and qualitative.Sequential colormaps show data that goes from low to high. Diverging colormaps highlight values that move away from a middle point. Qualitative colormaps use different colors to show categories without order.

🎯 Goal: You will generate and print colors using a sequential colormap in Matplotlib. This will help you see how colors change with data and how to pick the right colormap for your chart.

📋 What You'll Learn

Create a list of numbers from 0 to 9

Create a variable for a sequential colormap name

Generate colors from the colormap using normalized values from the numbers

Print the list of colors generated

💡 Why This Matters

🌍 Real World

Choosing the right colormap helps make data charts clear and easy to understand. For example, showing temperature changes or survey categories with colors.

💼 Career

Data scientists and analysts use colormaps to highlight important patterns and differences in data visualizations for reports and presentations.

1

Create a list of numbers from 0 to 9

Create a list called numbers that contains the integers from 0 to 9 inclusive.

Matplotlib

# Create a list called numbers with values 0 to 9
# Your code here

Hint

Use the range() function to create numbers from 0 to 9, then convert it to a list.

2

Set a sequential colormap name

Create a variable called seq_cmap and set it to the string 'Blues', which is a sequential colormap in Matplotlib.

Matplotlib

numbers = list(range(10))
# Set seq_cmap to 'Blues'
# Your code here

Hint

Just assign the string 'Blues' to the variable seq_cmap.

3

Generate colors from the sequential colormap

Import matplotlib.pyplot as plt. Use plt.get_cmap(seq_cmap) to get the colormap. Then create a list called colors by applying the colormap to each number normalized by dividing by 9 (the max number) for each n in numbers.

Matplotlib

numbers = list(range(10))
seq_cmap = 'Blues'
# Import matplotlib.pyplot as plt
# Get the colormap using plt.get_cmap(seq_cmap)
# Create colors list by applying cmap to n/9 for each n in numbers
# Your code here

Hint

Use a list comprehension to apply the colormap to each normalized number.

4

Print the list of colors

Print the variable colors to see the RGBA color values generated from the sequential colormap.

Matplotlib

import matplotlib.pyplot as plt

numbers = list(range(10))
seq_cmap = 'Blues'
cmap = plt.get_cmap(seq_cmap)
colors = [cmap(n / 9) for n in numbers]
# Print the colors list
# Your code here

Hint

Use print(colors) to display the list of colors.

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