Python Matplotlib - Stacked Bar Plots

Python Matplotlib - Stacked Bar Plots

Stacked bar plots are a great way to visualize the contribution of different components to a total. In Python, Matplotlib makes it easy to create and customize stacked bar charts. This tutorial explores how to create and enhance stacked bar plots with examples and detailed explanations.

Creating a Basic Stacked Bar Plot

In a stacked bar plot, bars are divided into segments to represent multiple datasets stacked on top of each other.

Example 1: Basic Stacked Bar Plot

import matplotlib.pyplot as plt
import numpy as np

# Data for the stacked bar plot
categories = ['Category A', 'Category B', 'Category C']
values1 = [3, 5, 2]
values2 = [4, 6, 3]

# Bar positions
x = np.arange(len(categories))

# Create the stacked bar plot
plt.bar(x, values1, label='Dataset 1', color='skyblue')
plt.bar(x, values2, bottom=values1, label='Dataset 2', color='orange')

# Add labels, title, and legend
plt.xticks(x, categories)
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Basic Stacked Bar Plot')
plt.legend()

# Show the plot
plt.show()

Explanation

  1. plt.bar() creates bars for values1 at positions x.
  2. The bottom parameter stacks values2 on top of values1.
  3. xticks maps the bar positions to category labels.
Basic Stacked Bar Plot

Customizing Stacked Bar Plots

Matplotlib allows extensive customization of stacked bar plots to improve aesthetics and readability.

Example 2: Customized Stacked Bar Plot

import matplotlib.pyplot as plt
import numpy as np

# Data for the stacked bar plot
categories = ['Category A', 'Category B', 'Category C']
values1 = [3, 5, 2]
values2 = [4, 6, 3]
values3 = [2, 4, 1]

# Bar positions
x = np.arange(len(categories))

# Create the stacked bar plot with custom styles
plt.bar(x, values1, label='Dataset 1', color='lightblue', edgecolor='black')
plt.bar(x, values2, bottom=values1, label='Dataset 2', color='lightgreen', edgecolor='black')
plt.bar(x, values3, bottom=np.array(values1) + np.array(values2), label='Dataset 3', color='lightcoral', edgecolor='black')

# Add labels, title, and legend
plt.xticks(x, categories)
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Customized Stacked Bar Plot')
plt.legend()

# Show the plot
plt.show()

Explanation

  1. Additional dataset values3 is stacked using the cumulative sum of values1 and values2.
  2. edgecolor adds a border to the bars for better distinction.
  3. Colors are customized for improved visualization.
Customized Stacked Bar Plot

Grouped Stacked Bar Plots

You can create grouped stacked bar plots to compare multiple groups within each category.

Example 3: Grouped Stacked Bar Plot

import matplotlib.pyplot as plt
import numpy as np

# Data for the grouped stacked bar plot
categories = ['Category A', 'Category B', 'Category C']
values_group1 = [3, 5, 2]
values_group2 = [4, 6, 3]
values_group3 = [2, 4, 1]

group_width = 0.8  # Total width of each group
bar_width = group_width / 3  # Width of each individual bar

# Bar positions for each group
x = np.arange(len(categories))
x_group1 = x - bar_width
x_group2 = x
x_group3 = x + bar_width

# Create the grouped stacked bar plot
plt.bar(x_group1, values_group1, label='Group 1', color='blue')
plt.bar(x_group2, values_group2, label='Group 2', color='green')
plt.bar(x_group3, values_group3, label='Group 3', color='red')

# Add labels, title, and legend
plt.xticks(x, categories)
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Grouped Stacked Bar Plot')
plt.legend()

# Show the plot
plt.show()

Explanation

  1. group_width determines the total width of the bars in a group, and bar_width divides it among individual bars.
  2. Bar positions are calculated separately for each group using x and offsets.
  3. Each group is styled and labeled distinctly for clarity.
Grouped Stacked Bar Plot

Summary

In this tutorial, we covered:

  • Creating basic stacked bar plots using Matplotlib.
  • Customizing stacked bar plots for better aesthetics.
  • Designing grouped stacked bar plots for comparative analysis.

Stacked bar plots are versatile tools for visualizing data distributions and comparisons across multiple categories. Explore these techniques to make your data more insightful and visually appealing.



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