Dashboard layout patterns in Matplotlib

Introduction

Dashboard layout patterns help organize charts and information clearly. They make it easy to understand data at a glance.

When you want to show multiple charts on one screen.

When you need to compare different data points side by side.

When you want to guide viewers through a story with data.

When you want to save space but keep information readable.

When you want to create a professional and clean report.

Syntax

Matplotlib

import matplotlib.pyplot as plt

fig, axs = plt.subplots(nrows, ncols, figsize=(width, height))

# Use axs[row, col] to plot each chart
axs[0, 0].plot(data1)
axs[0, 1].bar(data2)

plt.tight_layout()
plt.show()

plt.subplots() creates a grid of plots.

Use axs[row, col] to access each plot area.

Examples

This creates a dashboard with 1 row and 2 columns, showing a line chart and a bar chart side by side.

Matplotlib

fig, axs = plt.subplots(1, 2)
axs[0].plot([1, 2, 3])
axs[1].bar([1, 2, 3], [3, 2, 1])
plt.show()

This creates a 2 by 2 grid of histograms with adjusted spacing.

Matplotlib

fig, axs = plt.subplots(2, 2, figsize=(8, 6))
for i in range(2):
    for j in range(2):
        axs[i, j].hist([1, 2, 3, 4, 5])
plt.tight_layout()
plt.show()

Sample Program

This example creates a dashboard with 6 different charts arranged in 2 rows and 3 columns. Each chart has a clear title to explain what it shows.

Matplotlib

import matplotlib.pyplot as plt

fig, axs = plt.subplots(2, 3, figsize=(12, 6))

# First chart: line plot
axs[0, 0].plot([1, 2, 3, 4], [10, 20, 25, 30])
axs[0, 0].set_title('Sales Over Time')

# Second chart: bar plot
axs[0, 1].bar(['A', 'B', 'C'], [5, 7, 3])
axs[0, 1].set_title('Product Popularity')

# Third chart: scatter plot
axs[0, 2].scatter([1, 2, 3], [4, 5, 6])
axs[0, 2].set_title('Customer Visits')

# Fourth chart: pie chart
axs[1, 0].pie([30, 40, 30], labels=['X', 'Y', 'Z'], autopct='%1.1f%%')
axs[1, 0].set_title('Market Share')

# Fifth chart: histogram
axs[1, 1].hist([1, 2, 2, 3, 3, 3, 4, 4, 5])
axs[1, 1].set_title('Age Distribution')

# Sixth chart: boxplot
axs[1, 2].boxplot([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
axs[1, 2].set_title('Score Spread')

plt.tight_layout()
plt.show()

Important Notes

Use plt.tight_layout() to avoid overlapping labels and titles.

Choose layout size with figsize to make charts readable.

Always add titles to help viewers understand each chart quickly.

Summary

Dashboard layout patterns organize multiple charts clearly.

Use plt.subplots() to create grids of charts.

Adjust size and spacing for a clean, easy-to-read dashboard.