How to create a stack plot using matplotlib
The idea of stack plots is to show “parts to a whole” over time; basically, it’s like a pie-chart, only over time. Stack plots are mainly used to see various trends in variables over a specific period of time.
Matplotlib has a built-in function to create stack plots called
stackplot().
Syntax
Take a look at the syntax below:
matplotlib.pyplot.stackplot(x, y1, y2, ..., yn, c=None, ...)
x and y1,y2,…,yn are the data points on the x and y-axis respectively. The function also has several other optional parameters such as color, area, alpha values, etc.
Example
See the example below which portrays the 5-day routine of a person on a stack plot. Routine activities include sleep, eat, work, and exercise. Click run to see the graph.
import matplotlib.pyplot as pltdays = [1,2,3,4,5]sleep = [6,7,5,8,6]eat = [2,2,1,2,1]work = [5,7,10,8,6]exercise= [3,3,0,1,3]plt.plot([],[],color='green', label='sleep', linewidth=3)plt.plot([],[],color='blue', label='eat', linewidth=3)plt.plot([],[],color='red', label='work', linewidth=3)plt.plot([],[],color='black', label='play', linewidth=3)plt.stackplot(days, sleep, eat, work, exercise, colors=['green','blue','red','black'])plt.xlabel('days')plt.ylabel('activities')plt.title('5 DAY ROUTINE')plt.legend()plt.show()
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The first thing you do is import
matplotlib(line 1) -
Then, you create a variable,
days, which will represent our x-axis data (line 3) -
Next, you create the variables
sleep,eat,work, andexercisewith values that correspond to thedaysvalues (line 5 - 8) -
You can use the
plt.plot()function, where you specify the color and the label for each constituent, to add legend data (line 11 - 14) -
Then, you have the line
plt.stackplot(days, sleep, eat, work, exercise, colors=['green','blue','red','black'])with thedaysvariable as the x-axis. Next, thesleep,eat,work, andexercisevariables are plotted as y-axis data in the order specified. The colors are matched in a corresponding order for the y-values. (line 16) -
Then, you can add the legend, title, x-label, and y-label. (line 18 - 21)
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Finally, we show the plot (line 22)
