In this post, we are going to learn how to explore data using Python, Pandas, and Seaborn. The data we are going to explore is data from a Wikipedia article. In this post we are actually going to learn how to parse data from a URL using Python Pandas. Furthermore, we are going to explore the scraped data by grouping it and by Python data visualization. More specifically, we will learn how to count missing values, group data to calculate the mean, and then visualize relationships between two variables, among other things.
In previous posts, we have used Pandas to import data from Excel and CSV files. In this post, however, we are going to use Pandas read_html, because it has support for reading data from HTML from URLs (https or http). To read HTML Pandas use one of the Python libraries LXML, Html5Lib, or BeautifulSoup4. This means that you have to make sure that at least one of these libraries are installed. In the specific Pandas read_html example here, we use BeautifulSoup4 to parse the HTML tables from the Wikipedia article.