Colorbar configuration in Matplotlib - Step-by-Step Execution

Concept Flow - Colorbar configuration

Create plot with data

↓

Add color mapping to plot

↓

Call colorbar() function

↓

Configure colorbar properties

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Display plot with colorbar

The flow shows creating a plot, adding color mapping, calling colorbar(), configuring it, and displaying the final plot.

Execution Sample

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

img = plt.imshow(np.random.rand(5,5), cmap='viridis')
cbar = plt.colorbar(img, orientation='vertical', shrink=0.8)
plt.show()

This code creates a heatmap with a vertical colorbar that is shrunk to 80% of its default size.

Execution Table

Step	Action	Input/Parameter	Effect/Result
1	Create random data	np.random.rand(5,5)	5x5 array of random floats between 0 and 1
2	Plot data with imshow	cmap='viridis'	Heatmap image object created with viridis colors
3	Add colorbar	orientation='vertical', shrink=0.8	Colorbar added vertically, 80% size of default
4	Display plot	plt.show()	Plot window opens showing heatmap and configured colorbar

💡 Plot displayed with colorbar configured as specified

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
img	None	None	AxesImage object with data and colormap	Same	Same
cbar	None	None	None	Colorbar object with orientation vertical and shrink 0.8	Same

Key Moments - 2 Insights

Why does the colorbar shrink parameter affect its size?

The 'shrink' parameter scales the colorbar length relative to default. See execution_table step 3 where shrink=0.8 makes it 80% size.

What happens if orientation is set to 'horizontal'?

The colorbar will be drawn horizontally instead of vertically, changing its layout. This is controlled in execution_table step 3.

Visual Quiz - 3 Questions

Look at the execution_table, what type of object is 'img' after step 2?

A Colorbar object

B AxesImage object with data and colormap

C Numpy array

D Figure object

💡 Hint

Check the 'Effect/Result' column in row for step 2 in execution_table

At which step is the colorbar added to the plot?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look for the row mentioning 'Add colorbar' in execution_table

If we remove the 'shrink=0.8' parameter, how would the colorbar size change?

A It would be larger, default full size

B It would be smaller than 80%

C It would disappear

D It would become horizontal

💡 Hint

Refer to key_moments about the effect of the shrink parameter on colorbar size

Concept Snapshot

Colorbar configuration in matplotlib:
- Use plt.colorbar(mappable) to add colorbar
- Parameters: orientation ('vertical' or 'horizontal'), shrink (scale size)
- Colorbar reflects colormap of plotted data
- Adjust size and position with parameters
- Call plt.show() to display plot with colorbar

Full Transcript

This visual execution traces how to add and configure a colorbar in matplotlib. First, random data is created and plotted with imshow using a colormap. Then plt.colorbar is called with orientation and shrink parameters to add a vertical colorbar scaled to 80% size. Variables img and cbar hold the image and colorbar objects respectively. The plot is displayed with plt.show(). Key points include how the shrink parameter controls colorbar size and orientation changes layout. The execution table shows each step's action and result, helping beginners see how the colorbar is configured and added.