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

Concept Flow - Heatmap with plt.pcolormesh

Prepare 2D data array

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Create X, Y grid coordinates

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Call plt.pcolormesh(X, Y, data)

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Color cells based on data values

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Add colorbar for scale

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Show heatmap plot

This flow shows how to create a heatmap by preparing data, mapping it on a grid, coloring cells, and displaying the plot.

Execution Sample

Matplotlib

import numpy as np
import matplotlib.pyplot as plt

Z = np.array([[1, 2], [3, 4]])
plt.pcolormesh(Z, cmap='viridis')
plt.colorbar()
plt.show()

This code creates a simple 2x2 heatmap using pcolormesh with a color scale.

Execution Table

Step	Action	Input/State	Output/Result
1	Create 2D data array Z	None	[[1, 2], [3, 4]]
2	Call plt.pcolormesh(Z, cmap='viridis')	Z=[[1,2],[3,4]]	Grid cells colored by values
3	Add colorbar	Plot with colored cells	Color scale shown on side
4	Show plot	Plot ready	Heatmap displayed on screen
5	End	Plot displayed	Execution stops

💡 Plot is shown and program ends

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
Z	None	[[1, 2], [3, 4]]	[[1, 2], [3, 4]]	[[1, 2], [3, 4]]	[[1, 2], [3, 4]]
Plot	None	None	Heatmap grid created	Colorbar added	Displayed

Key Moments - 3 Insights

Why does plt.pcolormesh require a 2D array for data?

Because each cell in the heatmap corresponds to one value in the 2D array, as shown in execution_table step 2 where the array Z defines cell colors.

What does the colorbar represent in the heatmap?

The colorbar shows the mapping from data values to colors, helping interpret the heatmap colors, as added in step 3 of the execution_table.

Why do we call plt.show() at the end?

plt.show() displays the plot window so we can see the heatmap, as indicated in step 4 where the heatmap is displayed on screen.

Visual Quiz - 3 Questions

Look at the execution_table at step 2, what is the input data used for coloring the heatmap cells?

A [[4, 3], [2, 1]]

B [[1, 2], [3, 4]]

C [1, 2, 3, 4]

D None

💡 Hint

Check the 'Input/State' column at step 2 in execution_table.

At which step in the execution_table is the colorbar added to the plot?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look for the action mentioning 'Add colorbar' in execution_table.

If we change the data array Z to [[5, 6], [7, 8]], how would the heatmap colors change?

A Colors would represent higher values, shifting the color scale

B Colors would stay the same

C Heatmap would become empty

D Colorbar would disappear

💡 Hint

Refer to variable_tracker for Z values and how they map to colors in execution_table step 2.

Concept Snapshot

plt.pcolormesh(data, cmap) creates a heatmap by coloring grid cells based on 2D data values.
Data must be a 2D array where each value colors one cell.
Add plt.colorbar() to show the color scale.
Call plt.show() to display the heatmap plot.
Useful for visualizing matrices or grids of values.

Full Transcript

This visual execution trace shows how to create a heatmap using matplotlib's plt.pcolormesh. First, a 2D data array Z is created with values. Then plt.pcolormesh is called with Z and a color map to color each grid cell according to its value. Next, a colorbar is added to show the mapping from values to colors. Finally, plt.show() displays the heatmap plot on screen. Variables like Z and the plot state are tracked through each step. Key moments clarify why data must be 2D, the role of the colorbar, and the need to call plt.show(). The quiz tests understanding of data input, colorbar addition, and effect of changing data values on colors. This step-by-step trace helps beginners see how heatmaps are built and displayed visually.