The first parameter it receives is the text we would like to annotate. To achieve that, we can use the ax annotatemethod. We need to identify to which programming language each of the lines corresponds. A NumPy array can be created by np.arange(). Additionally, we can set the y ticks by passing a NumPy array with the values to plt.yticks(). We could go about it by using the command plt.gca().invert_yaxis(). Besides that, we would like all the rank numbers to be shown on the y-axis. It would be nice to have the number one language at the top of the chart. Inverting the y-axis and setting axis ticks Image created by the Author using Matplotlib 6. for element in list_programming: ax.plot(element, element, "o-", # format of marker / format of line markerfacecolor="white") The result is almost what we want, but further adjustments are needed. Note that the marker face color was set to white, meaning the dot is filled with white. Moreover, you can choose the style of the marker and line with “o-” indicating we would like a line with a dot as the marker. Calling the plot method for each programming languageįor each dictionary in our list, we will call the ax plot method specifying the years on the x-axis and the ranks on the y-axis. We will set it to be 12 inches wide and 6 inches high. In Matplotlib you may change the size of your plot using plt.rcParams. import matplotlib.pyplot as plt import numpy as np fig, ax = plt.subplots() 4. The code below loads the necessary libraries and creates the two objects just mentioned. The Figure object will be the container of your plot, while the Axes object will be the plot itself. This method creates two objects: one object of the class Figureand one of the class Axes. There are several ways you can create a plot with Matplotlib, but to get flexibility, it is recommended to use subplots(). To make reproducibility easier, the data is code generated and stored in a list of dictionaries, as shown below. This post will use the Octoverse data as an example. Another ranking is Stack Overflow’s 2020 Developer Survey, which presents similar but not identical results. There are several rankings using different data and methods to estimate the position of each language. Rust and Typescript were the second and third in growth, respectively. The report also revealed that the Hashicorp Configuration Language (HCL) was the fastest-growing language on GitHub reflecting the expansion of cloud infrastructure. JavaScript was the most used language followed by Python, the language we will use to build our bump chart. Our competitors: the programming languagesĪccording to Octoverse, in 2022, programmers used around 500 languages to develop software on GitHub. Image created by the Author using Matplotlib 2. The line and its color represent the singer, the x-axis represents the year, and, the y-axis, the rank. Imagine, for example, that each line in the figure below represents the rank of a singer’s popularity. What is a bump chart?Ī bump chart is similar to a line plot but focused on exploring changes in rank over time. As an example, you will use data published by the Octoverse Report 2022, which analyzed the most popular programming languages in 2022. In this lesson, you will learn how to show changes in rank with basic Matplotlib, with no need for additional libraries. ![]() ![]() But as with everything in life, ranks change all the time. What are the best football teams in 2023? Who was the best tennis player in 2022 and what was the most used programming language on GitHub last year? We want to know what’s trending right now. Humans are always comparing, evaluating, and ranking all aspects of life. I found out, for instance, that on the week I was born, on August 1987, I Still Haven’t Found What I’m Looking For was the number one song on the Hot Billboard 100! I even liked to check which songs were popular in the past. Despite that, it was a great source of new and good songs for me. Usually, my favorite songs ended up failing to reach the top positions. When I was a teenager I loved to check the most popular songs on Billboard, even though my taste was often different from what Billboard presented to me.
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