Stacked area chart python

Matplotlib Series 7: Area chart

This blog specifies how to create simple area charts, multiple area charts, stacked area charts and 100% stacked area charts with matplotlib in Python, and their use cases.

• Matplotlib Series 1: Bar chart
• Matplotlib Series 2: Line chart
• Matplotlib Series 3: Pie chart
• Matplotlib Series 4: Scatter plot
• Matplotlib Series 5: Treemap
• Matplotlib Series 6: Venn diagram
• Matplotlib Series 7: Area chart (this blog)
• Matplotlib Series 8: Radar chart
• Matplotlib Series 9: Word cloud
• Matplotlib Series 10: Lollipop plot
• Matplotlib Series 11: Histogram

Area chart

An area chart or area graph displays graphically quantitative data. It is based on the line chart. The area between axis and line are commonly emphasized with colors, textures and hatchings.

When to use it ?

Example 1

As an extension of the first plot, the second one compares two-year turnover together. The peak for both year is in the summer, for year N, the peak is in August; however, the peak is reached in June in the year N-1, which is caused by the heatwave in June.

Stacked area chart

Stacked Area Graphs work in the same way as simple Area Graphs do, except for the use of multiple data series that start each point from the point left by the previous data series.

When to use it ?

• Represent cumulated totals using numbers or percentages over time.
• Visualize part-to-whole relationships, helping show how each category contributes to the cumulative total.

Example

This stacked area chart displays the amounts’ changes in each account, their contribution to total amount (in term of percentage) as well.

You can click here to check this example in jupyter notebook.

Reference

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Stacked area Chart

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.

The most basic stacked area chart one can make with python and matplotlib

# 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?

It is hard to see how the green group evolves.

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Stacked Area Charts

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Introduction

Stacked charts are a great opportunity to showcase relevant values in the same graph next to each other.

I particularly enjoy analyzing and creating stacked area charts because they show the historical evolution of subjects and we can also see the what the values add up to when combined.

In this visualization tutorial we will learn how to create stacked area charts using Python and Matplotlib.

1- Matplotlib’s Stackplot and Python Libraries

stackplot() is the function that can be used to create stacked area charts. It’s usage is pretty straightforward. We need data sequences for x-axis and values that share the y-axis concurrently. It will be something like below:

Let’s gear up the Python libraries we may want to use for this task.

import matplotlib.pyplot as plt import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns plt.style.use("seaborn") 

As usual we will start with the core functions in this tutorial and then make a more advanced example to demonstrate stacked area charts using Python and its libraries.

2- Example 1: Basic Stackplot Example

We will need data for 3 line objects. They can share the same x-axis values and move in sync horizontally but we will need 3 different set of y values for them.

Let’s create some data using List Comprehension and random library’s randint function.

Data:

coal = [8447, 8881, 8886, 9324, 9408, 9124, 9147, 9401, 9716, 9453] gas = [4761, 4817, 5084, 4978, 5128, 5467, 5695, 5787, 5959, 6186] hydro = [3422, 3491, 3656, 3797, 3888, 3889, 4032, 4075, 4190, 4246] nuclear = [2725, 2612, 2434, 2454, 2502, 2536, 2572, 2591, 2657, 2756] oil = [946, 1060, 1130, 1087, 1045, 1058, 1004, 927, 921, 861] solar = [32, 63, 98, 136, 197, 256, 328, 445, 578, 711] wind = [346, 440, 530, 639, 713, 829, 961, 1137, 1265, 1417] year = [i+2010 for i in range(10)] 

Stacked Area Chart:

colors = sns.color_palette("RdBu", 7) labels=["coal", "gas", "oil", "nuclear", "hydro", "wind", "solar"] plt.stackplot(year, coal, gas, oil, nuclear, hydro, wind, solar, labels=labels, colors=colors) 

Plot Aesthetics:

plt.legend(loc = "upper center", bbox_to_anchor=(1.1, 0.8), ncol=1) plt.title('Electricity by Source') plt.ylabel('TWh') plt.xticks(np.arange(2010,2020,step=1), rotation=40) plt.show() 

Unfortunately, fossil fuels still consist of the largest group from which electricity is generated and especially coal has a very large share. Wind and solar power generation growth in second half of the decade is encouraging while hydro, nuclear and oil remain stable.

3- Example 2: Evolution of CO2 emissions

We can make a slightly more complex example to demonstrate the true capabilities of Stackplot.

In this example let’s create a stacked area chart using CO2 emission data by different countries and regions or clusters of countries.

This example will be great to demonstrate how insightful and visually aesthetic stacked charts can be.

Basically, the stackplot will work on the same principals as the previous example but we will have a more sophisticated data stream.

You can find the data used below in this link. When we have larger datasets it makes sense to take advantage of Python’s pandas library which offers amazing tools to contain and manipulate data frames data series and matrice.

First we will need to read the csv file obtained from Our World in Data. read_csv function works well for this task. Then we can use Numpy’s unique function to have a collection of unique year values from the data frame. (Because if you open csv you will see that year values are repeated for each country, this would cause a problem of mismatch with y values when we draw the stackplot chart.)

Reading CSV File:

df = pd.read_csv("Desktop/annual-co.csv") year = np.unique(data['Year'].values) 

After opening the csv file and assigning year values to the year variable, we can also work on the y values of the stacked chart.

Below you can see how pandas data frame is filtered to only get the required values for each y-axis object.

For example, first line consist of dataset that will be used to draw China’s CO2 emissions in recent history. You can see that column “Annual CO2 emission” is called where column “Entity” equals “China”.

Similarly we will use CO2 emissions for the US, EU, Africa, India, South America, Oceania and International Transport values.

Data from Pandas Dataframes:

chi = (df[df['Entity']=="China"]['Annual CO2 emissions']) usa = (df[df['Entity']=="United States"]['Annual CO2 emissions']) eu27 = (df[df['Entity']=="EU-27"]['Annual CO2 emissions']) afr = (df[df['Entity']=="Africa"]['Annual CO2 emissions']) ind = (df[df['Entity']=="India"]['Annual CO2 emissions']) sa = (df[df['Entity']=="South America"]['Annual CO2 emissions']) eunon27 = (df[df['Entity']=="Europe (excl. EU-27)"]['Annual CO2 emissions']) oce = (df[df['Entity']=="Oceania"]['Annual CO2 emissions']) transport = (df[df['Entity']=="International transport"]['Annual CO2 emissions']) 

The rest is pretty much the same but also let’s create a color palette using seaborn library and its color_palette function. Spectral palette should be ideal for this task since it’s a diverging color map that starts with red colors and ends up with blue colors. We can slice it to 9 colors (since we have 9 different stacks) and we can obtain hex codes from it using the code below:

Colors from Seaborn color palette:

palette = sns.color_palette("Spectral", 9).as_hex() colors = ','.join(palette) labels = ("China", "USA", "EU27", "EUnon27", "India", "Africa", "transport", "SA", "Oceania") 

We’re also including label names to make the stacked chart more meaningful. Now we have many elements of the stackplot ready. We have prepared: data (for x-axis and y-axes), colors (stacked areas) and labels (for countries the make up the stacked areas).

We can finally use the stackplot function with these.

Charting with Stackplot

fig = plt.figure(figsize=(8,5)) plt.stackplot(year, chi, usa, eu27, eunon27, ind, afr, transport, sa, oce, colors=colors, labels=labels) plt.legend(loc='upper center', bbox_to_anchor=(1.1, 0.8), shadow=True, ncol=1) plt.xticks(np.arange(1750,2020,step=10), rotation=40) 

Above, one single stacked chart has so much to say. Here are some points from the chart above:

• Throughout 18th century and first half of 19th century CO2 emissions from human activity was nearly inexistent relative to recent values.
• Total global CO2 emissions have been growing exponentially since 1930s
• China has seen an astronomical surge in its total CO2 emissions since early 2000s.
• India, Africa, South America and International Transport CO2 emissions are other items on the chart that have been growing recently.

Another important question to ask is: “What does this data not tell?”. For example, here we don’t see the emissions per capita which can be an important criteria while evaluating countries’ CO2 emission performance and trend. Also, lots of Asian countries are missing which can be another point to mislead the analysts or readers.

Hopefully, this example was useful for demonstrating stacked area charts. If you liked it or find it useful feel free to share and spread the coding love.

Summary

In this visualization tutorial we learned how to create stacked area charts using matplotlib’s stackplot function.

Additionally we used a few other useful functions such as numpy’s unique and seaborn’s color_palette and pandas’ read_csv function. You can see that high level coding is as much about making use of different libraries and their functions in harmony as it is about coding syntax and logic.

Like spoken languages, programming languages are as useful as you use them for connecting the dots, while building with passion and coming up with creative solutions.

plt.legend and plt.xtick were used to manage legend of the chart as well as ticks on the x-axis (plt stands for pyplot). We adjusted the position of the legend and the rotation of the tick values as well as their frequency and start-end points.

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