Daily Python

One of Python’s most distinctive features is the list comprehension, which you can use to create powerful functionality within a single line of code. However, many developers struggle to fully leverage the more advanced features, or use them too much, which can lead to code that’s less efficient and harder to read.

By the end of this course, you’ll understand the full power of Python list comprehensions and how to use their features comfortably. You’ll also gain an understanding of the trade-offs that come with using them so that you can determine when other approaches are more preferable.

In this course, you’ll learn how to:

Rewrite loops and map() calls as a list comprehension in Python
Choose between comprehensions, loops, and map() calls
Supercharge your comprehensions with conditional logic
Use comprehensions to replace filter()
Profile your code to solve performance questions

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

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Introduction

There are many data visualization libraries in Python, yet Matplotlib is the most popular library out of all of them. Matplotlib’s popularity is due to its reliability and utility - it's able to create both simple and complex plots with little code. You can also customize the plots in a variety of ways.

In this tutorial, we'll cover how to plot Stack Plots in Matplotlib.

Stack Plots are used to plot linear data, in a vertical order, stacking each linear plot on another. Typically, they're used to generate cumulative plots.

Importing Data

We'll be using a dataset on Covid-19 vaccinations, from Our World in Data, specifically, the dataset that contains the cumulative vaccinations per country.

We’ll begin by importing all the libraries that we need. We’ll import Pandas to read and parse the dataset, Numpy to generate values for the X-axis, and we’ll of course need to import the PyPlot module from Matplotlib:

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

Let's take a peak at the DataFrame we'll be using:

dataframe = pd.read_csv("cumulative-covid-vaccinations.csv")
print(dataframe.head(25))

We're interested in the Entity and total_vaccinations. While we could use the Date feature as well, to gain a better grasp of how the vaccinations are going day-by-day, we'll treat the first entry as Day 0 and the last entry as Day N:

     Entity Code        Date  total_vaccinations
0   Albania  ALB  2021-01-10                   0
1   Albania  ALB  2021-01-12                 128
2   Albania  ALB  2021-01-13                 188
3   Albania  ALB  2021-01-14                 266
4   Albania  ALB  2021-01-15                 308
5   Albania  ALB  2021-01-16                 369
...
16  Albania  ALB  2021-02-22                6728
17  Albania  ALB  2021-02-25               10135
18  Albania  ALB  2021-03-01               14295
19  Albania  ALB  2021-03-03               15793
20  Albania  ALB  2021-03-10               21613
21  Algeria  DZA  2021-01-29                   0
22  Algeria  DZA  2021-01-30                  30
23  Algeria  DZA  2021-02-19               75000
24  Andorra  AND  2021-01-25                 576

This dataset will require some pre-processing, since this is a specific use-case. Though, before pre-processing it, let's get acquainted with how Stack Plots are generally plotted.

Plot a Stack Plot in Matplotlib

Stack Plots are used to visualize multiple linear plots, stacked on top of each other. With a regular line plot, you'd plot the relationship between X and Y. Here, we're plotting multiple Y features on a shared X-axis, one on top of the other:

import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y1 = [5, 6, 4, 5, 7]
y2 = [1, 6, 4, 5, 6]
y3 = [1, 1, 2, 3, 2]

fig, ax = plt.subplots()
ax.stackplot(x, y1, y2, y3)
plt.show()

This results in:

simple stack plot

Since it's a bit unwieldy to deal with multiple lists like this, you can simply use a dictionary, where each yn feature is an entry:

import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]

y_values = {
    "y1": [5, 6, 4, 5, 7],
    "y2": [1, 6, 4, 5, 6],
    "y3" : [1, 1, 2, 3, 2]
}

fig, ax = plt.subplots()
ax.stackplot(x, y_values.values())
plt.show()

This results in:

simple stack plot matplotlib

Since this type of plot can easily get you lost in the stacks, it's really helpful to add labels attached to the colors, by setting the keys() from the y_values dictionary as the labels argument, and adding a legend to the plot:

import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]

y_values = {
    "y1": [5, 6, 4, 5, 7],
    "y2": [1, 6, 4, 5, 6],
    "y3" : [1, 1, 2, 3, 2]
}

fig, ax = plt.subplots()
ax.stackplot(x, y_values.values(), labels=y_values.keys())
ax.legend(loc='upper left')
plt.show()

Now, this results in:

matplotlib stack plot with legend

Note: The length of these lists has to be the same. You can't plot y1 with 3 values, and y2 with 5 values.

This brings us to our Covid-19 vaccination dataset. We'll pre-process the dataset to take the form of a dictionary like this, and plot the cumulative vaccines given to the general population.

Let's start off by grouping the dataset by Entity and total_vaccinations, since each Entity currently has numerous entries. Also, we'll want to drop the entities named World and European Union, since they're convenience entities, added for cases where you might want to plot just a single cumulative line.

In our case, it'll effectively more than double the total_vaccination count, since they include already plotted values of each country, as single entities:

dataframe = pd.read_csv("cumulative-covid-vaccinations.csv")
indices = dataframe[(dataframe['Entity'] == 'World') | (dataframe['Entity'] == 'European Union')].index
dataframe.drop(indices, inplace=True)

countries_vaccinations = dataframe.groupby('Entity')['total_vaccinations'].apply(list)

This results in a completely different shape of the dataset - instead of each entry having their own Entity/total_vaccinations entry, each Entity will have a list of their total vaccinations through the days:

Entity
Albania           [0, 128, 188, 266, 308, 369, 405, 447, 483, 51...
Algeria                                              [0, 30, 75000]
Andorra           [576, 1036, 1291, 1622, 2141, 2390, 2526, 3611...
...
Croatia           [7864, 12285, 13798, 20603, 24985, 30000, 3455...
Cyprus            [3901, 6035, 10226, 17739, 25519, 32837, 44429...
Czechia           [1261, 3560, 7017, 10496, 11813, 12077, 13335,...

Now, let's convert this Series into a dictionary and see what it looks like:

cv_dict = countries_vaccinations.to_dict()
print(cv_dict)

This results in:

{
'Albania': [0, 128, 188, 266, 308, 369, 405, 447, 483, 519, 549, 550, 1127, 1701, 3049, 4177, 6728, 10135, 14295, 15793, 21613], 
'Algeria': [0, 30, 75000], 
'Andorra': [576, 1036, 1291, 1622, 2141, 2390, 2526, 3611, 4914],
...
}

However, there's a problem here. We can't plot these entries if their shapes aren't the same. Algeria has 3 entries, while Andorra has 9, for example. To combat this, we'll want to find the key with the most values, and how many values there are.

Then, construct a new dictionary (inadvisable to modify original dictionary while iterating through it) and insert 0s for each missing day in the past, since there were 0 total vaccinations at those days:

max_key, max_value = max(cv_dict.items(), key = lambda x: len(set(x[1])))

cv_dict_full = {}
for k,v in cv_dict.items():
    if len(v) < len(max_value):
        trailing_zeros = [0]*(len(max_value)-len(v))
        cv_dict_full[k] = trailing_zeros+v
    else:
        cv_dict_full[k] = v
        
print(cv_dict_full)

Here, we simply check if the length of the list in each entry is shorter than the length of the list with the maximum length. If it is, we add the difference between those, in zeros, and append that value to the original list of values.

Now, if we print this new dictionary, we'll see something along the lines of:

{
'Albania': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 128, 188, 266, 308, 369, 405, 447, 483, 519, 549, 550, 1127, 1701, 3049, 4177, 6728, 10135, 14295, 15793, 21613],
'Algeria': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 75000],
'Andorra': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 576, 1036, 1291, 1622, 2141, 2390, 2526, 3611, 4914],
...
}

The country with the most input values is:

print(max_key, len(max_value)) # Canada 90

Now that we've fully prepared our dataset, and we can plot it as we've plotted the Stack Plots before it, let's generate the days and plot:

dates = np.arange(0, len(max_value))

fig, ax = plt.subplots()
ax.stackplot(dates, cv_dict_full.values(), labels=cv_dict_full.keys())
ax.legend(loc='upper left', ncol=4)
ax.set_title('Cumulative Covid Vaccinations')
ax.set_xlabel('Day')
ax.set_ylabel('Number of people')

plt.show()

Since there's a lot of countries in the world, the legend will be fairly crammed, so we've put it into 4 columns to at least fit in the plot:

matplotlib stackplot covid cases

Conclusion

In this tutorial, we've gone over how to plot simple Stack Plots, as well as how to pre-process datasets and shape data to fit Stack Plots, using Python's Pandas and Matplotlib frameworks.

If you're interested in Data Visualization and don't know where to start, make sure to check out our book on Data Visualization in Python.

Data Visualization in Python, a book for beginner to intermediate Python developers, will guide you through simple data manipulation with Pandas, cover core plotting libraries like Matplotlib and Seaborn, and show you how to take advantage of declarative and experimental libraries like Altair.

Data Visualization in Python

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Daily Python

Tuesday, March 30, 2021

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Introduction

Importing Data

Plot a Stack Plot in Matplotlib

Conclusion

Data Visualization in Python

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