Dashboard layout patterns in Matplotlib - Step-by-Step Execution

Concept Flow - Dashboard layout patterns

Start: Create Figure

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Define Grid or Subplots

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Assign Plots to Grid Cells

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Customize Each Plot

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Adjust Layout Spacing

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Display Dashboard

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End

This flow shows how to build a dashboard by creating a figure, defining a grid layout, placing plots, customizing them, adjusting spacing, and finally displaying the dashboard.

Execution Sample

Matplotlib

import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 2)
axs[0, 0].plot([1, 2, 3])
axs[0, 1].bar([1, 2, 3], [3, 2, 1])
plt.tight_layout()
plt.show()

This code creates a 2x2 dashboard with a line plot and a bar chart, then adjusts spacing and shows the dashboard.

Execution Table

Step	Action	Result/State	Plot Area Affected
1	Import matplotlib.pyplot as plt	plt module ready	None
2	Create figure and 2x2 subplots	Figure with 4 axes created	All 4 grid cells
3	Plot line on axs[0,0]	Line plot added	Top-left cell
4	Plot bar on axs[0,1]	Bar chart added	Top-right cell
5	Call plt.tight_layout()	Layout spacing adjusted	All cells
6	Call plt.show()	Dashboard displayed	All cells
7	End of execution	Dashboard visible with 2 plots	All cells

💡 All plots placed and dashboard displayed successfully

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	After Step 5	Final
fig	None	Figure object created	Figure object unchanged	Figure object unchanged	Figure object unchanged	Figure object unchanged
axs	None	2x2 array of Axes objects	Line plot on axs[0,0]	Bar plot on axs[0,1]	Layout adjusted	Dashboard displayed

Key Moments - 3 Insights

Why do we use plt.tight_layout() after adding plots?

plt.tight_layout() adjusts spacing so plots don't overlap, as shown in step 5 of the execution_table.

What happens if we try to plot on axs[1,1] without creating a 2x2 grid?

There would be an error because axs[1,1] does not exist; step 2 shows the grid creation that defines valid axes.

Why do we assign fig and axs when calling plt.subplots()?

fig is the overall figure container, axs holds the individual plot areas; step 2 shows their creation and usage.

Visual Quiz - 3 Questions

Look at the execution_table, what is the state of axs after step 4?

A 2x2 array with a line plot on axs[0,0] and a bar plot on axs[0,1]

B Empty 2x2 array with no plots

C Single plot object with line and bar combined

D Figure object without axes

💡 Hint

Check the 'Result/State' column for step 4 in execution_table

At which step does the dashboard become visible to the user?

A Step 3

B Step 5

C Step 6

D Step 2

💡 Hint

Look for the action 'plt.show()' in execution_table

If we remove plt.tight_layout(), what likely happens to the dashboard layout?

A Plots overlap or have uneven spacing

B Dashboard does not display

C Plots become invisible

D Dashboard shows with perfect spacing

💡 Hint

Refer to key_moments about the purpose of plt.tight_layout()

Concept Snapshot

Dashboard layout patterns in matplotlib:
- Use plt.subplots() to create grid of plots
- Assign plots to each subplot (axs[row,col])
- Use plt.tight_layout() to fix spacing
- Call plt.show() to display dashboard
- Each subplot is an independent plot area

Full Transcript

This lesson shows how to create dashboard layouts using matplotlib. We start by importing matplotlib.pyplot. Then we create a figure with a grid of subplots using plt.subplots(2, 2). Each subplot is accessed by axs[row, col]. We add different plots to these subplots, like a line plot and a bar chart. After adding plots, we call plt.tight_layout() to adjust spacing so plots do not overlap. Finally, plt.show() displays the dashboard with all plots arranged in the grid. Variables fig and axs hold the figure and axes objects respectively. This step-by-step process helps beginners see how dashboard layouts are built visually.