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.