Heatmap with plt.imshow in Matplotlib - Step-by-Step Execution

Concept Flow - Heatmap with plt.imshow

Prepare 2D data array

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Call plt.imshow(data)

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Render heatmap colors

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Add colorbar (optional)

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Show plot with plt.show()

The flow shows how a 2D data array is passed to plt.imshow, which renders it as a colored heatmap, optionally adding a colorbar, then displays the plot.

Execution Sample

Matplotlib

import matplotlib.pyplot as plt
import numpy as np
data = np.array([[1, 2], [3, 4]])
heatmap_image = plt.imshow(data, cmap='viridis')
colorbar = plt.colorbar()
plt.show()

This code creates a 2x2 heatmap from a small 2D array using the 'viridis' color map and shows a colorbar.

Execution Table

Step	Action	Input/Parameter	Result/Output
1	Create 2D numpy array	[[1, 2], [3, 4]]	data array ready
2	Call plt.imshow	data, cmap='viridis'	Heatmap image object created
3	Call plt.colorbar	None	Colorbar linked to heatmap
4	Call plt.show	None	Heatmap with colorbar displayed
5	End	Plot window closed or script ends	Execution stops

💡 Plot displayed and script ends, no further steps

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
data	None	[[1 2] [3 4]]	[[1 2] [3 4]]	[[1 2] [3 4]]	[[1 2] [3 4]]
heatmap_image	None	None	AxesImage object	AxesImage object	AxesImage object
colorbar	None	None	None	Colorbar object	Colorbar object

Key Moments - 3 Insights

Why does plt.imshow need a 2D array as input?

plt.imshow uses the 2D array values to assign colors for each cell in the heatmap, as shown in execution_table step 2 where the data array is passed to create the heatmap image.

What does the cmap parameter do in plt.imshow?

The cmap parameter sets the color scheme for the heatmap. In step 2, 'viridis' is used to map data values to colors, making the heatmap visually meaningful.

Why do we call plt.colorbar after plt.imshow?

plt.colorbar links a color scale to the heatmap image, helping interpret colors. This is shown in step 3 where the colorbar is created after the heatmap image exists.

Visual Quiz - 3 Questions

Look at the execution table, what is the type of the object created by plt.imshow at step 2?

A Figure object

B Colorbar object

C AxesImage object

D Numpy array

💡 Hint

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

At which step is the colorbar linked to the heatmap?

A Step 3

B Step 1

C Step 2

D Step 4

💡 Hint

Look for when plt.colorbar is called in the execution_table.

If the data array was 3x3 instead of 2x2, how would the heatmap change?

A The heatmap would stay 2x2 but colors would change

B The heatmap would have 3 rows and 3 columns of colored cells

C The heatmap would be a single color

D The code would error out

💡 Hint

Refer to variable_tracker for how data shape affects the heatmap size.

Concept Snapshot

plt.imshow(data, cmap='color_map')
- Displays 2D array as colored heatmap
- cmap sets color scheme (e.g., 'viridis')
- Use plt.colorbar() to add legend
- plt.show() renders the plot
- Data shape controls heatmap grid size

Full Transcript

This visual execution traces how to create a heatmap using matplotlib's plt.imshow. First, a 2D numpy array is prepared as data. Then plt.imshow is called with this data and a color map to create a heatmap image object. Next, plt.colorbar adds a color scale legend linked to the heatmap. Finally, plt.show displays the heatmap plot. Variables like the data array, heatmap image, and colorbar objects are tracked through each step. Key beginner questions about input data, color maps, and colorbars are answered referencing the execution steps. A quiz tests understanding of object types, step order, and data shape effects. The snapshot summarizes the main commands and their roles in making a heatmap with plt.imshow.