Custom colormaps in Matplotlib - Mini Project: Build & Apply

Custom colormaps

📖 Scenario: You are working with a heatmap that shows temperature data for a week. You want to make the colors more meaningful by creating your own color scale.

🎯 Goal: Create a custom colormap using matplotlib and apply it to a heatmap of temperature data.

📋 What You'll Learn

Create a 2D list called temperatures with exact values for 7 days and 3 times per day

Create a custom colormap named my_cmap using LinearSegmentedColormap.from_list with specified colors

Use plt.imshow with my_cmap to display the heatmap

Print the heatmap with plt.colorbar() to show the color scale

💡 Why This Matters

🌍 Real World

Custom colormaps help make data visualizations clearer and more meaningful, especially when showing temperature, elevation, or other continuous data.

💼 Career

Data scientists and analysts often create custom colormaps to highlight important data ranges and improve reports or dashboards.

1

Create the temperature data

Create a 2D list called temperatures with these exact values: [[22, 24, 19], [21, 23, 20], [20, 22, 18], [19, 21, 17], [18, 20, 16], [17, 19, 15], [16, 18, 14]]

Matplotlib

# Create the 2D list temperatures with the exact values
# Your code here

Hint

Use square brackets to create a list of lists with the exact numbers.

2

Create the custom colormap

Import LinearSegmentedColormap from matplotlib.colors and create a colormap called my_cmap using LinearSegmentedColormap.from_list with the colors ['blue', 'white', 'red'].

Matplotlib

temperatures = [[22, 24, 19], [21, 23, 20], [20, 22, 18], [19, 21, 17], [18, 20, 16], [17, 19, 15], [16, 18, 14]]
# Import LinearSegmentedColormap and create my_cmap with colors ['blue', 'white', 'red']
# Your code here

Hint

Use from matplotlib.colors import LinearSegmentedColormap and then call LinearSegmentedColormap.from_list with the name and color list.

3

Plot the heatmap with the custom colormap

Import matplotlib.pyplot as plt. Use plt.imshow to display temperatures with the colormap my_cmap. Then add a colorbar with plt.colorbar().

Matplotlib

from matplotlib.colors import LinearSegmentedColormap

temperatures = [[22, 24, 19], [21, 23, 20], [20, 22, 18], [19, 21, 17], [18, 20, 16], [17, 19, 15], [16, 18, 14]]

my_cmap = LinearSegmentedColormap.from_list('my_cmap', ['blue', 'white', 'red'])
# Import matplotlib.pyplot as plt and plot temperatures with my_cmap
# Your code here

Hint

Use plt.imshow with the cmap argument set to my_cmap. Then call plt.colorbar() to show the scale.

4

Show the heatmap

Add plt.show() to display the heatmap with the custom colormap and colorbar.

Matplotlib

from matplotlib.colors import LinearSegmentedColormap
import matplotlib.pyplot as plt

temperatures = [[22, 24, 19], [21, 23, 20], [20, 22, 18], [19, 21, 17], [18, 20, 16], [17, 19, 15], [16, 18, 14]]

my_cmap = LinearSegmentedColormap.from_list('my_cmap', ['blue', 'white', 'red'])

plt.imshow(temperatures, cmap=my_cmap)
plt.colorbar()
# Add plt.show() to display the plot
# Your code here

Hint

Use plt.show() to open the window with the heatmap.

Practice

1. What is the main purpose of creating a custom colormap in matplotlib?

easy

A. To speed up the plotting process

B. To automatically label axes

C. To choose colors that better represent your data and improve chart clarity

D. To change the plot size

Solution

1. Step 1: Understand what colormaps do

   Colormaps assign colors to data values in a plot to help visualize differences.

2. Step 2: Identify the benefit of custom colormaps

   Custom colormaps let you pick colors that fit your data and style, making charts clearer and more attractive.

3. Final Answer:

   To choose colors that better represent your data and improve chart clarity -> Option C

4. Quick Check:

   Custom colormaps improve data visualization clarity = B [OK]

Hint: Custom colormaps improve how data colors show up [OK]

Common Mistakes:

• Thinking colormaps speed up plotting
• Confusing colormaps with axis labels
• Assuming colormaps change plot size

2. Which of the following is the correct way to create a custom colormap from a list of colors in matplotlib?

easy

A. LinearSegmentedColormap.from_list('mycmap', ['red', 'green', 'blue'])

B. plt.colormap(['red', 'green', 'blue'])

C. color_map(['red', 'green', 'blue'])

D. create_colormap(['red', 'green', 'blue'])

Solution

1. Step 1: Recall the correct function for custom colormaps

   Matplotlib uses LinearSegmentedColormap.from_list to create colormaps from color lists.

2. Step 2: Check the syntax

   The correct syntax is LinearSegmentedColormap.from_list('mycmap', ['red', 'green', 'blue']). Other options are not valid matplotlib functions.

3. Final Answer:

   LinearSegmentedColormap.from_list('mycmap', ['red', 'green', 'blue']) -> Option A

4. Quick Check:

   Use LinearSegmentedColormap.from_list for custom colormaps = A [OK]

Hint: Use LinearSegmentedColormap.from_list with color list [OK]

Common Mistakes:

• Using plt.colormap which does not exist
• Trying to call create_colormap which is not a function
• Confusing function names

3. What will be the output of this code snippet?

import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap

colors = ['yellow', (1, 0.5, 0), 'red']
my_cmap = LinearSegmentedColormap.from_list('mycmap', colors)
print(my_cmap(0.5))

medium

A. Error: Invalid colormap

B. (0.5, 0.5, 0.5, 1.0)

C. (1.0, 0.0, 0.0, 1.0)

D. (1.0, 0.5, 0.0, 1.0)

Solution

1. Step 1: Understand colormap interpolation

   The colormap is created from yellow, (1, 0.5, 0), and red. At 0.5 (middle), it should be the middle anchor color (1.0, 0.5, 0.0).

2. Step 2: Check RGBA values for the middle color

   The middle color exactly corresponds to (1.0, 0.5, 0.0, 1.0) in RGBA format, which matches the output.

3. Final Answer:

   (1.0, 0.5, 0.0, 1.0) -> Option D

4. Quick Check:

   Middle color in colormap = orange RGBA = A [OK]

Hint: Middle of colormap returns middle color RGBA [OK]

Common Mistakes:

• Assuming 0.5 returns gray
• Confusing color order
• Expecting an error from valid code

4. Identify the error in this code that tries to create a custom colormap:

from matplotlib.colors import LinearSegmentedColormap
colors = ['blue', 'white', 'green']
my_cmap = LinearSegmentedColormap(colors)

medium

A. LinearSegmentedColormap requires 'from_list' method to create from colors

B. Colors list must be strings of hex codes only

C. You must import pyplot to create colormaps

D. The colors list cannot have more than two colors

Solution

1. Step 1: Check how to create colormaps from colors

   LinearSegmentedColormap is a class and cannot be called directly with a list. Instead, use the class method from_list.

2. Step 2: Identify the correct usage

   The correct way is LinearSegmentedColormap.from_list('name', colors). The code misses from_list.

3. Final Answer:

   LinearSegmentedColormap requires 'from_list' method to create from colors -> Option A

4. Quick Check:

   Use from_list to create colormap from colors = D [OK]

Hint: Use from_list method to create colormap from colors [OK]

Common Mistakes:

• Calling LinearSegmentedColormap directly
• Thinking colors must be hex codes
• Importing pyplot unnecessarily

5. You want to create a custom colormap that smoothly transitions from blue to white to red, but with the white color occupying the middle 40% of the range. Which approach correctly achieves this?

hard

A. Use LinearSegmentedColormap.from_list('bwr', ['blue', 'white', 'red'])

B. Use LinearSegmentedColormap.from_list('bwr', ['blue', 'white', 'white', 'red'], [0, 0.3, 0.7, 1])

C. Use LinearSegmentedColormap.from_list('bwr', ['blue', 'white', 'red'], [0, 0.5, 1])

D. Use LinearSegmentedColormap.from_list('bwr', ['blue', 'red']) and set midpoint to white separately

Solution

1. Step 1: Understand color position control in colormaps

   To make white occupy the middle 40%, you need to specify positions for colors explicitly.

2. Step 2: Analyze the options

   Use LinearSegmentedColormap.from_list('bwr', ['blue', 'white', 'white', 'red'], [0, 0.3, 0.7, 1]) uses four colors with positions [0, 0.3, 0.7, 1], placing white twice to stretch it over 40% (from 0.3 to 0.7). This creates a smooth transition with white in the middle range.

3. Final Answer:

   Use LinearSegmentedColormap.from_list('bwr', ['blue', 'white', 'white', 'red'], [0, 0.3, 0.7, 1]) -> Option B

4. Quick Check:

   Specify color positions to stretch middle color = C [OK]

Hint: Repeat middle color with positions to stretch it [OK]

Common Mistakes:

• Not specifying positions, so white is evenly spread
• Using only three colors without position list
• Trying to set midpoint color separately