Stacked area chart in Matplotlib

Introduction

A stacked area chart helps you see how different parts add up to a whole over time or categories.

To show how sales from different products contribute to total sales over months.

To visualize how different expenses add up to total spending over years.

To track how different website traffic sources combine to total visits daily.

To display how population groups contribute to total population growth over decades.

Syntax

Matplotlib

import matplotlib.pyplot as plt

# Data for x-axis (categories or time)
x = [x1, x2, x3, ...]

# Data for each group stacked on top of each other
y1 = [y11, y12, y13, ...]
y2 = [y21, y22, y23, ...]
y3 = [y31, y32, y33, ...]

plt.stackplot(x, y1, y2, y3, labels=['Group1', 'Group2', 'Group3'])
plt.legend(loc='upper left')
plt.show()

The x values must be in order (like time or categories).

Each y list must have the same length as x.

Examples

Basic example with two groups over four points.

Matplotlib

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
y1 = [1, 2, 3, 4]
y2 = [2, 1, 2, 1]

plt.stackplot(x, y1, y2, labels=['A', 'B'])
plt.legend()
plt.show()

Edge case: empty data shows no areas.

Matplotlib

import matplotlib.pyplot as plt

x = []
y1 = []
y2 = []

plt.stackplot(x, y1, y2, labels=['Empty1', 'Empty2'])
plt.legend()
plt.show()

Edge case: only one data point.

Matplotlib

import matplotlib.pyplot as plt

x = [1]
y1 = [5]
y2 = [3]

plt.stackplot(x, y1, y2, labels=['Single1', 'Single2'])
plt.legend()
plt.show()

Example where one group is larger at the start and the other at the end.

Matplotlib

import matplotlib.pyplot as plt

x = [1, 2, 3]
y1 = [1, 2, 3]
y2 = [3, 2, 1]

plt.stackplot(x, y1, y2, labels=['StartHeavy', 'EndHeavy'])
plt.legend()
plt.show()

Sample Program

This program shows monthly sales for three products as a stacked area chart. It prints the sales data before plotting so you can see the numbers.

Matplotlib

import matplotlib.pyplot as plt

# Data: months and sales from 3 products
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May']
product_a_sales = [100, 120, 130, 90, 110]
product_b_sales = [80, 90, 100, 95, 105]
product_c_sales = [50, 60, 70, 65, 80]

print('Sales before plotting:')
print('Months:', months)
print('Product A:', product_a_sales)
print('Product B:', product_b_sales)
print('Product C:', product_c_sales)

# Convert months to numbers for x-axis
x_values = list(range(len(months)))

plt.stackplot(x_values, product_a_sales, product_b_sales, product_c_sales, labels=['Product A', 'Product B', 'Product C'])
plt.xticks(x_values, months)  # Show month names on x-axis
plt.title('Monthly Sales by Product')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.legend(loc='upper left')
plt.show()

Important Notes

Time complexity is O(n) where n is the number of data points.

Space complexity is O(n) for storing the data arrays.

Common mistake: x and y arrays must be the same length, or matplotlib will error.

Use stacked area charts to show part-to-whole relationships over time, not for comparing individual groups alone.

Summary

Stacked area charts show how parts add up to a whole over categories or time.

Make sure x and y data have matching lengths and x is ordered.

Use labels and legends to make the chart easy to understand.