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.

Practice

1. What does a stacked area chart in matplotlib primarily show?

easy

A. How different parts add up to a whole over categories or time

B. Only the total sum of all data points

C. The relationship between two unrelated variables

D. The frequency distribution of a single variable

Solution

1. Step 1: Understand the purpose of stacked area charts

   Stacked area charts display multiple data series stacked on top of each other to show how each part contributes to the total over time or categories.

2. Step 2: Compare options with this purpose

   How different parts add up to a whole over categories or time correctly describes this purpose. Options A, B, and C describe other chart types or incorrect uses.

3. Final Answer:

   How different parts add up to a whole over categories or time -> Option A

4. Quick Check:

   Stacked area chart = parts adding to whole [OK]

Hint: Stacked area charts show parts stacked to form a whole [OK]

Common Mistakes:

• Confusing stacked area chart with line chart
• Thinking it shows only totals, not parts
• Mixing it up with scatter plots

2. Which of the following is the correct way to create a stacked area chart using matplotlib.pyplot?

easy

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

B. plt.area(x, y1, y2)

C. plt.plot_area(x, y1, y2)

D. plt.stack(x, y1, y2)

Solution

1. Step 1: Recall the correct function name

   The correct function to create stacked area charts in matplotlib is stackplot from matplotlib.pyplot.

2. Step 2: Check the syntax

   plt.stackplot(x, y1, y2, labels=['A', 'B']) uses plt.stackplot(x, y1, y2, labels=['A', 'B']), which is correct. Other options use non-existent functions.

3. Final Answer:

   plt.stackplot(x, y1, y2, labels=['A', 'B']) -> Option A

4. Quick Check:

   Function name is stackplot [OK]

Hint: Use plt.stackplot for stacked area charts [OK]

Common Mistakes:

• Using plt.area which does not exist
• Trying plt.plot_area instead of stackplot
• Forgetting to pass x and multiple y arrays

3. What will be the output of this code snippet?

import matplotlib.pyplot as plt
x = [1, 2, 3]
y1 = [1, 2, 3]
y2 = [2, 1, 2]
plt.stackplot(x, y1, y2, labels=['A', 'B'])
plt.legend()
plt.show()

medium

A. An error because y arrays have different lengths

B. A line chart with two lines labeled 'A' and 'B'

C. A bar chart with two bars for each x value

D. A stacked area chart with two layers labeled 'A' and 'B' over x=1 to 3

Solution

1. Step 1: Analyze the data lengths and function

   Both y1 and y2 have length 3 matching x. The function plt.stackplot creates a stacked area chart.

2. Step 2: Understand the output

   The code plots two stacked areas labeled 'A' and 'B' over x values 1 to 3, then shows a legend.

3. Final Answer:

   A stacked area chart with two layers labeled 'A' and 'B' over x=1 to 3 -> Option D

4. Quick Check:

   Stackplot with matching lengths = stacked area chart [OK]

Hint: Check lengths of x and y arrays match for stackplot [OK]

Common Mistakes:

• Thinking stackplot creates line or bar charts
• Assuming error due to lengths when they match
• Ignoring legend labels

4. Identify the error in this code that tries to plot a stacked area chart:

import matplotlib.pyplot as plt
x = [1, 2, 3]
y1 = [1, 2]
y2 = [2, 1, 2]
plt.stackplot(x, y1, y2)
plt.show()

medium

A. Missing labels argument causes the plot to fail

B. plt.stackplot does not accept multiple y arrays

C. x and y1 have different lengths causing a ValueError

D. plt.show() is missing parentheses

Solution

1. Step 1: Check lengths of x and y arrays

   x has length 3, y1 has length 2, y2 has length 3. Length mismatch causes error.

2. Step 2: Understand stackplot requirements

   All y arrays must have the same length as x. This mismatch causes a ValueError.

3. Final Answer:

   x and y1 have different lengths causing a ValueError -> Option C

4. Quick Check:

   Length mismatch in stackplot = error [OK]

Hint: Ensure all y arrays match x length in stackplot [OK]

Common Mistakes:

• Assuming labels are required to avoid errors
• Thinking stackplot accepts different length arrays
• Forgetting to call plt.show() with parentheses

5. You have monthly sales data for three products over 6 months:

months = [1, 2, 3, 4, 5, 6]
product_a = [5, 7, 8, 6, 7, 9]
product_b = [3, 4, 2, 5, 6, 4]
product_c = [2, 3, 4, 3, 2, 3]

How would you create a stacked area chart showing each product's contribution over time with a legend?

hard

A. plt.plot(months, product_a + product_b + product_c)
plt.legend(['Total'])
plt.show()

B. plt.stackplot(months, product_a, product_b, product_c, labels=['A', 'B', 'C'])
plt.legend()
plt.show()

C. plt.bar(months, product_a, bottom=product_b)
plt.show()

D. plt.stackplot(product_a, product_b, product_c, labels=['A', 'B', 'C'])
plt.legend()
plt.show()

Solution

1. Step 1: Use plt.stackplot with x and multiple y arrays

   Pass months as x and product sales as y arrays with labels for legend.

2. Step 2: Add legend and show plot

   Call plt.legend() to show labels and plt.show() to display the chart.

3. Final Answer:

   plt.stackplot(months, product_a, product_b, product_c, labels=['A', 'B', 'C']) plt.legend() plt.show() -> Option B

4. Quick Check:

   Stackplot with labels + legend = stacked area chart [OK]

Hint: Pass x, multiple y arrays, labels, then call plt.legend() [OK]

Common Mistakes:

• Adding y arrays instead of stacking
• Using bar chart with wrong bottom parameter
• Forgetting to pass x as first argument