Colormaps (sequential, diverging, qualitative) in Matplotlib - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a sequential colormap in matplotlib?

A sequential colormap shows a range of colors that go from light to dark or from low to high intensity. It is used for data that has order, like numbers increasing from small to large.

beginner

Describe a diverging colormap and when to use it.

A diverging colormap has two contrasting colors that meet in the middle with a neutral color. It is good for data with a meaningful center point, like temperatures above and below zero.

beginner

What is a qualitative colormap used for?

Qualitative colormaps use distinct colors without order. They are used to show categories or groups, like different types of fruits or countries.

beginner

Give an example of a sequential colormap in matplotlib.

An example is 'viridis', which goes from dark purple to yellow. It is good for showing increasing values clearly.

intermediate

Why should you choose a diverging colormap for data with a midpoint?

Because it highlights differences above and below the midpoint with two colors, making it easy to see where values are higher or lower than the center.

Which colormap type is best for showing categories without order?

A Sequential

B Continuous

C Diverging

D Qualitative

Qualitative colormaps use distinct colors for categories without implying order.

What does a sequential colormap represent?

A Data with a meaningful center point

B Ordered data from low to high

C Categorical data

D Random colors

Sequential colormaps show ordered data, usually from low to high values.

Which matplotlib colormap is an example of a diverging colormap?

A coolwarm

B tab10

C viridis

D plasma

'coolwarm' is a diverging colormap with two contrasting colors and a neutral center.

When should you use a diverging colormap?

A For data with a meaningful midpoint

B For data with no order

C For categorical data

D For random data

Diverging colormaps highlight values above and below a center point.

Which colormap type is best for showing increasing values clearly?

A Diverging

B Qualitative

C Sequential

D Categorical

Sequential colormaps are designed to show ordered data increasing from low to high.

Explain the differences between sequential, diverging, and qualitative colormaps and give an example of when to use each.

Think about data types: ordered numbers, data with a center, and categories.

Describe how you would choose a colormap for a heatmap showing temperature differences from freezing point.

Focus on the importance of the center value in your data.

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