You can benefit the seaborn style in your graphs by calling the set_theme[] function of seaborn library at the beginning of your code:
# libraries
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# set seaborn style
sns.set_theme[]
# Data
x=range[1,6]
y=[ [1,4,6,8,9], [2,2,7,10,12], [2,8,5,10,6] ]
# Plot
plt.stackplot[x,y, labels=['A','B','C']]
plt.legend[loc='upper left']
plt.show[]A stacked area chart displays the evolution of a numeric variable for several groups of a dataset. Each group is displayed on top of each other, making it easy to read the evolution of the total, but hard to read each group value accurately. In python, stacked area charts are mainly done thanks to the stackplot[] function
⏱ Quick start
Here is a quick start code
snippet to demo how the stackplot[] function of matplotlib works.
Note that here each groups are provided in its own vector of values. The basic stacked area blog post explains how to use the function from any type of data format.
# library
import numpy as np
import matplotlib.pyplot as plt
# Create data
x=range[1,6]
y1=[1,4,6,8,9]
y2=[2,2,7,10,12]
y3=[2,8,5,10,6]
# Basic stacked area chart.
plt.stackplot[x,y1, y2, y3, labels=['A','B','C']]
plt.legend[loc='upper left']
⚠️ The issue with stacking
Stacked area charts must be used with care since they suffer a number of caveats. They are appropriate to study the evolution of the whole and the relative proportions of each group, but not to study the evolution of each individual group.
For instance, it is pretty hard to understand how the green group evolves on the chart below. Can you spot if its value is increasing, decreasing or stable?
Stacked Area chart with Matplotlib
Matplotlib is the most common way to build a stacked area chart with Python. The examples below
start by explaining to basics of the stackplot[] function. The also describe the most common type of customization like changing colors, controling group order and more.
💡 The baseline parameter
It is important to note that the stackplot[] function of matplotlib has abaseline parameter. This parameter controls how groups are displayed around the x axis, what allows to mimic a
streamgraph.
Percent Stacked Area chart with Matplotlib
A variation of the stacked area graph is the percent stacked area graph where the value of every groups are normalized at each time stamp. It allows to study the percentage of each group in the whole more efficiently.
Fortunately, the pandas library has a divide[] function that allows to apply this normalization
easily.
Stacked Area chart with Pandas
Pandas is mainly useful to normalize your dataset and build a stacked area chart. Surprisingly, it also provides a plot.area[]that can be handy to build a stacked area chart.
From the web
The web is full of astonishing charts made by awesome bloggers, [often using R]. The Python graph gallery tries to display [or translate from R] some of the best creations and explain how their source code works. If you want to display your work here, please drop me a word or even better, submit a Pull Request!
You can use the following basic syntax to create an area chart in seaborn:
import matplotlib.pyplot as plt import seaborn as sns #set seaborn style sns.set_theme[] #create seaborn area chart plt.stackplot[df.x, df.y1, df.y2, df.y3]
The following examples show how to use this syntax in practice.
Example 1: Create Basic Area Chart in Seaborn
The following code shows how to create a basic area chart in seaborn:
import pandas as pd import matplotlib.pyplot as plt import seaborn as sns #set seaborn style sns.set_theme[] #define DataFrame df = pd.DataFrame[{'period': [1, 2, 3, 4, 5, 6, 7, 8], 'team_A': [20, 12, 15, 14, 19, 23, 25, 29], 'team_B': [5, 7, 7, 9, 12, 9, 9, 4], 'team_C': [11, 8, 10, 6, 6, 5, 9, 12]}] #create area chart plt.stackplot[df.period, df.team_A, df.team_B, df.team_C]
The x-axis displays the period variable and the y-axis displays the values for each of the three teams over time.
Example 2: Create Custom Area Chart in Seaborn
The following code shows how to modify the colors of the area chart and add a legend with specific labels:
import pandas as pd import matplotlib.pyplot as plt import seaborn as sns #set seaborn style sns.set_theme[] #define DataFrame df = pd.DataFrame[{'period': [1, 2, 3, 4, 5, 6, 7, 8], 'team_A': [20, 12, 15, 14, 19, 23, 25, 29], 'team_B': [5, 7, 7, 9, 12, 9, 9, 4], 'team_C': [11, 8, 10, 6, 6, 5, 9, 12]}] #define colors to use in chart color_map = ['red', 'steelblue', 'pink'] #create area chart plt.stackplot[df.period, df.team_A, df.team_B, df.team_C, labels=['Team A', 'Team B', 'Team C'], colors=color_map] #add legend plt.legend[loc='upper left'] #add axis labels plt.xlabel['Period'] plt.ylabel['Points Scored'] #display area chart plt.show[]
Note that the colors argument accepts color names along with hex color codes.
Additional Resources
The following tutorials explain how to create other common plots in seaborn:
How to Create a Time Series Plot in Seaborn
How to Create a Pie Chart
in Seaborn
How to Create a Barplot in Seaborn