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.

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