Color mapping with colorbar in Matplotlib - Step-by-Step Execution

Concept Flow - Color mapping with colorbar

Create data values

↓

Choose colormap

↓

Map data to colors

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Plot data with colors

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Add colorbar to show scale

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Display plot

This flow shows how data values are linked to colors using a colormap, then plotted with a colorbar to explain the color scale.

Execution Sample

Matplotlib

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

values = np.array([1, 2, 3, 4, 5])
colors = plt.cm.viridis(values / 5)

plt.scatter(values, values, color=colors)
plt.colorbar(plt.cm.ScalarMappable(cmap='viridis', norm=Normalize(0, 5)))
plt.show()

This code maps numeric values to colors using the 'viridis' colormap, plots them, and adds a colorbar to show the color scale.

Execution Table

Step	Action	Data/Variable	Result/Output
1	Create data array	values	[1 2 3 4 5]
2	Normalize values for colormap	values / 5	[0.2 0.4 0.6 0.8 1.0]
3	Map normalized values to colors	plt.cm.viridis(values / 5)	Array of RGBA colors
4	Plot scatter points with colors	plt.scatter	Scatter plot with colored points
5	Create ScalarMappable for colorbar	ScalarMappable(cmap='viridis', norm=Normalize(0, 5))	Colorbar object linked to data range
6	Add colorbar to plot	plt.colorbar	Colorbar displayed alongside plot
7	Show plot	plt.show()	Plot window appears with points and colorbar
8	End	-	Execution complete

💡 All steps executed; plot with color-mapped points and colorbar displayed.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
values	-	[1 2 3 4 5]	[1 2 3 4 5]	[1 2 3 4 5]	[1 2 3 4 5]
normalized_values	-	-	[0.2 0.4 0.6 0.8 1.0]	[0.2 0.4 0.6 0.8 1.0]	[0.2 0.4 0.6 0.8 1.0]
colors	-	-	-	Array of RGBA colors	Array of RGBA colors

Key Moments - 3 Insights

Why do we divide values by 5 before applying the colormap?

Dividing by 5 normalizes the data to the range 0 to 1, which is required because colormaps expect input values between 0 and 1. See execution_table step 2.

What does the ScalarMappable do for the colorbar?

ScalarMappable links the colormap and normalization to the colorbar, so it knows how to map data values to colors. This is shown in execution_table step 5.

Why do the colors in the scatter plot match the colorbar scale?

Because both use the same colormap and normalization, the colors of points correspond exactly to the colorbar's color scale. See steps 3 and 6.

Visual Quiz - 3 Questions

Look at the execution_table step 2, what is the normalized value for the original data value 3?

A 0.6

B 0.3

C 3

D 1.5

💡 Hint

Check the 'Data/Variable' column in step 2 showing values divided by 5.

At which step is the colorbar object created?

A Step 3

B Step 6

C Step 5

D Step 4

💡 Hint

Look at the 'Action' column describing ScalarMappable creation.

If we change the normalization range from (0,5) to (1,5), what happens to the color mapping?

A Colors stay the same, no change

B Colors shift to include lower values, changing the colorbar scale

C Plot will error due to invalid normalization

D Colorbar disappears

💡 Hint

Normalization controls how data values map to colors; changing range affects colorbar and colors.

Concept Snapshot

Color mapping links data values to colors using a colormap.
Normalize data to 0-1 before mapping.
Plot data points with mapped colors.
Add a colorbar using ScalarMappable to show color scale.
Colorbar helps interpret colors in the plot.

Full Transcript

This visual execution shows how to map numeric data to colors using matplotlib's colormap. First, data values are created and normalized to a 0 to 1 range because colormaps expect inputs in this range. Then, these normalized values are converted to RGBA colors using a chosen colormap, here 'viridis'. The colored points are plotted using scatter. To explain the color meaning, a colorbar is added by creating a ScalarMappable object that links the colormap and normalization. Finally, the plot with colored points and the colorbar is displayed. Key moments include understanding normalization, the role of ScalarMappable, and how colors correspond between points and the colorbar.