Custom colormaps in Matplotlib - Step-by-Step Execution

Concept Flow - Custom colormaps

Define colors or color list

↓

Create colormap object

↓

Use colormap in plot

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Display plot with custom colors

You start by choosing colors, then create a colormap object, use it in a plot, and finally display the plot with your custom colors.

Execution Sample

Matplotlib

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

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

x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.scatter(x, y, c=y, cmap=cm)
plt.colorbar()
plt.show()

This code creates a custom colormap from blue to white to red and uses it to color points in a scatter plot based on their y-values.

Execution Table

Step	Action	Input/Variable	Output/Result
1	Define colors list	colors = ['blue', 'white', 'red']	colors list created
2	Create colormap	LinearSegmentedColormap.from_list('my_cmap', colors)	cm = custom colormap object
3	Generate data	x = np.linspace(0,10,100), y = np.sin(x)	x and y arrays created
4	Plot scatter	plt.scatter(x, y, c=y, cmap=cm)	Scatter plot with colors from cm
5	Add colorbar	plt.colorbar()	Colorbar shown with custom colors
6	Show plot	plt.show()	Plot window opens with colored scatter
7	End	No more code	Execution stops

💡 All steps completed, plot displayed and execution ends

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	Final
colors	undefined	['blue', 'white', 'red']	['blue', 'white', 'red']	['blue', 'white', 'red']	['blue', 'white', 'red']	['blue', 'white', 'red']
cm	undefined	undefined	custom colormap object	custom colormap object	custom colormap object	custom colormap object
x	undefined	undefined	undefined	array of 100 floats from 0 to 10	array of 100 floats from 0 to 10	array of 100 floats from 0 to 10
y	undefined	undefined	undefined	array of 100 sin values	array of 100 sin values	array of 100 sin values

Key Moments - 3 Insights

Why do we use LinearSegmentedColormap.from_list instead of just a list of colors?

Because matplotlib needs a colormap object to map numeric values to colors smoothly. The list alone is just colors, but from_list creates a colormap that interpolates between them, as shown in execution_table step 2.

How does the scatter plot know which color to use for each point?

The scatter plot uses the 'c' argument with y-values and the custom colormap 'cm' to map each y-value to a color. This mapping happens in execution_table step 4.

What does plt.colorbar() do with a custom colormap?

It shows a color scale that matches the custom colormap, helping us understand which colors correspond to which data values, as seen in execution_table step 5.

Visual Quiz - 3 Questions

Look at the execution_table, what is the value of 'cm' after step 2?

A A list of colors

B A custom colormap object

C An array of numbers

D Undefined

💡 Hint

Check the 'Output/Result' column for step 2 in execution_table.

At which step does the scatter plot get created with the custom colormap?

A Step 3

B Step 5

C Step 4

D Step 6

💡 Hint

Look for the action 'Plot scatter' in execution_table.

If you changed the colors list to ['green', 'yellow'], what would change in the variable_tracker?

A The 'colors' variable after step 1 would be ['green', 'yellow']

B The 'cm' variable would become undefined

C The 'x' and 'y' variables would change

D No variables would change

💡 Hint

Check how 'colors' is tracked in variable_tracker after step 1.

Concept Snapshot

Custom colormaps let you define your own color gradients.
Use matplotlib.colors.LinearSegmentedColormap.from_list(name, colors) to create one.
Pass this colormap to plotting functions with the cmap parameter.
Use plt.colorbar() to show the color scale.
This helps visualize data with colors you choose.

Full Transcript

This lesson shows how to create and use custom colormaps in matplotlib. First, you define a list of colors you want in your gradient. Then, you create a colormap object using LinearSegmentedColormap.from_list. Next, you generate some data and plot it using a scatter plot, applying your custom colormap to color the points based on their values. Adding plt.colorbar() displays the color scale. Finally, plt.show() opens the plot window. The execution table traces each step, showing how variables like colors, cm, x, and y change. Key moments clarify why a colormap object is needed, how colors map to data points, and the role of the colorbar. The quiz tests your understanding of these steps and variable changes. This visual trace helps beginners see exactly how custom colormaps work in practice.