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: To combat this, we'll want to find the key with the most values, and how many values there are. Algeria has 3 entries, while Andorra has 9, for example. We can't plot these entries if their shapes aren't the same. Now, let's convert this Series into a dictionary and see what it looks like: cv_dict = countries_vaccinations.to_dict() Indices = dataframe = 'World') | (dataframe = 'European Union')].indexĬountries_vaccinations = oupby( 'Entity').apply( list) 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") Here, we're plotting multiple Y features on a shared X-axis, one on top of the other: import matplotlib.pyplot as plt With a regular line plot, you'd plot the relationship between X and Y. Stack Plots are used to visualize multiple linear plots, stacked on top of each other. Though, before preprocessing it, let's get acquainted with how Stack Plots are generally plotted. This dataset will require some preprocessing, since this is a specific use-case. We're interested in the Entity and total_vaccinations. Let's take a peek at the DataFrame we'll be using: dataframe = pd.read_csv( "cumulative-covid-vaccinations.csv") 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 We’ll begin by importing all the libraries that we need. 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. Typically, they're used to generate cumulative plots. Stack Plots are used to plot linear data, in a vertical order, stacking each linear plot on another. In this tutorial, we'll cover how to plot Stack Plots in Matplotlib. You can also customize the plots in a variety of ways. Matplotlib’s popularity is due to its reliability and utility - it's able to create both simple and complex plots with little code. There are many data visualization libraries in Python, yet Matplotlib is the most popular library out of all of them.
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