Colormaps (sequential, diverging, qualitative) in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Sequential Colormap Gradient Plot

What will be the color of the middle bar in this horizontal bar plot using the 'Blues' sequential colormap?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

values = np.linspace(0, 1, 5)
colors = plt.cm.Blues(values)

plt.barh(range(5), [1]*5, color=colors)
plt.show()

# Question: What color corresponds to the middle bar (index 2) in terms of blue intensity?

A A medium blue, clearly visible but not dark

B A very light blue, close to white

C A very dark blue, almost navy

D Pure white

💡 Hint

Sequential colormaps go from light to dark as values increase from 0 to 1.

The 'Blues' colormap starts with very light blue at 0 and ends with dark blue at 1. The middle value (0.5) corresponds to a medium blue color.

🧠 Conceptual

intermediate

Purpose of Diverging Colormaps

Why are diverging colormaps useful in data visualization?

A They only use shades of gray to represent data.

B They highlight differences from a central neutral value, showing two extremes distinctly.

C They assign random colors to categories without order.

D They show a smooth gradient from light to dark of a single color.

💡 Hint

Think about data that has a meaningful midpoint like zero or average.

Diverging colormaps are designed to emphasize deviation from a midpoint, such as zero, by using two contrasting colors on either side.

❓ visualization

advanced

Identify Qualitative Colormap Usage

Which plot correctly uses a qualitative colormap to display categorical data?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

categories = ['A', 'B', 'C', 'D']
values = [5, 7, 3, 4]
colors = plt.cm.Set3(np.linspace(0, 1, 4))

plt.bar(categories, values, color=colors)
plt.show()

A A bar plot with distinct colors for each category using 'Set3' colormap

B A line plot with a gradient color from light to dark blue

C A heatmap using 'coolwarm' diverging colormap

D A scatter plot with colors representing a continuous variable

💡 Hint

Qualitative colormaps are best for categories without order.

The 'Set3' colormap is qualitative and provides distinct colors for categorical data, making it ideal for bar plots with categories.

🔧 Debug

advanced

Error in Using Colormap for Continuous Data

What error or issue will occur with this code snippet?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

values = np.array([1, 2, 3, 4])
colors = plt.cm.Set3(values / 4)

plt.scatter(values, values, color=colors)
plt.show()

A ValueError because 'colors' array shape is incorrect

B No error, plot shows correctly with distinct colors

C ValueError because 'Set3' expects values between 0 and 1, but got integers

D RuntimeWarning about deprecated colormap usage

💡 Hint

Colormaps expect input values normalized between 0 and 1.

Qualitative colormaps like 'Set3' expect input values between 0 and 1. Passing integers outside this range causes a ValueError.

🚀 Application

expert

Choosing the Best Colormap for Temperature Anomaly Data

You have temperature anomaly data ranging from -5 to +5 degrees. Which colormap choice best helps visualize both negative and positive anomalies clearly?

A Use a sequential colormap like 'Blues' from light to dark blue

B Use a grayscale colormap from black to white

C Use a qualitative colormap like 'Pastel1' with distinct colors

D Use a diverging colormap like 'coolwarm' centered at zero

💡 Hint

Think about data with meaningful midpoint zero and two extremes.

Diverging colormaps like 'coolwarm' highlight values below and above zero with contrasting colors, making it ideal for temperature anomalies.