Stacked area chart in Matplotlib - Step-by-Step Execution

Concept Flow - Stacked area chart

Prepare data series

↓

Calculate cumulative sums

↓

Plot areas stacked by cumulative values

↓

Add labels and legend

↓

Display chart

We start with data series, compute cumulative sums to stack them, then plot each area on top of the previous, add labels, and show the chart.

Execution Sample

Matplotlib

import matplotlib.pyplot as plt

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

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

This code creates a stacked area chart with two data series labeled A and B over x values.

Execution Table

Step	Action	Data State	Plot State	Output
1	Prepare x and y data	x=[1,2,3,4], y1=[1,3,2,4], y2=[2,1,3,1]	No plot yet	No output
2	Calculate cumulative sums for stacking	Cum y1 = y1, Cum y2 = y1 + y2 = [3,4,5,5]	Ready to plot stacked areas	No output
3	Plot first area y1	N/A	Area for y1 plotted from 0 to y1	Partial stacked area chart
4	Plot second area y2	N/A	Area for y2 plotted from y1 to y1+y2	Stacked area chart with two layers
5	Add legend with labels 'A' and 'B'	N/A	Legend added	Stacked area chart with legend
6	Display chart	N/A	Chart rendered on screen	Visible stacked area chart

💡 All data series plotted and chart displayed

Variable Tracker

Variable	Start	After Step 2	After Step 4	Final
x	[1, 2, 3, 4]	[1, 2, 3, 4]	[1, 2, 3, 4]	[1, 2, 3, 4]
y1	[1, 3, 2, 4]	[1, 3, 2, 4]	[1, 3, 2, 4]	[1, 3, 2, 4]
y2	[2, 1, 3, 1]	[2, 1, 3, 1]	[2, 1, 3, 1]	[2, 1, 3, 1]
cum_y1	N/A	[1, 3, 2, 4]	[1, 3, 2, 4]	[1, 3, 2, 4]
cum_y2	N/A	[3, 4, 5, 5]	[3, 4, 5, 5]	[3, 4, 5, 5]

Key Moments - 3 Insights

Why do we add the second data series to the first when plotting?

Because stacking means each area starts where the previous one ended, so we add y2 values to y1 to get the top boundary of the second area, as shown in execution_table step 2.

What happens if the x values are not sorted?

The areas may look incorrect or jumbled because the plot assumes x is ordered. The code uses x=[1,2,3,4] sorted, ensuring smooth stacking as in variable_tracker.

Why do we use plt.stackplot instead of plt.plot?

plt.stackplot automatically stacks multiple data series as filled areas, while plt.plot draws lines. This is why in execution_table step 3 and 4, areas are filled cumulatively.

Visual Quiz - 3 Questions

Look at the execution_table step 2, what is the cumulative sum of y2 at x=3?

A 2

B 3

C 5

D 4

💡 Hint

Check the 'Data State' column in step 2 for cum_y2 values.

At which step is the legend added to the chart?

A Step 5

B Step 4

C Step 3

D Step 6

💡 Hint

Look at the 'Action' column describing adding legend.

If y2 values were all zeros, how would the stacked area chart change?

A Both areas would be visible but overlapping

B Only one area for y1 would be visible

C Chart would be empty

D Areas would be plotted but not stacked

💡 Hint

Think about stacking when second series is zero, referencing variable_tracker y2 values.

Concept Snapshot

Stacked area chart:
- Use plt.stackplot(x, y1, y2, ..., labels=...)
- Each area stacks on top of previous by cumulative sum
- x must be sorted for correct plotting
- Add plt.legend() for labels
- Call plt.show() to display

Full Transcript

A stacked area chart shows multiple data series stacked on top of each other as filled areas. We start by preparing x and y data. Then we calculate cumulative sums of y series to stack them. Using matplotlib's plt.stackplot, we plot each area from zero or the previous cumulative sum. We add labels with plt.legend and finally display the chart with plt.show. This visual trace shows each step from data preparation to final chart rendering.

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