(Read herefor a more in-depth discussion of how the Python visualization libraries fit together.) When working with text data, the nltk and TextBlob libraries are useful for analysis and visualization. While matplotlibis the main graphics library, there are additional Python libraries focused on visualization, including making interactive plots/charts, 3D images, maps, and more. Like R, Python has libraries to make impressive visualizations. For those working with text data, the tidytext and tm packages are good options for cleaning, analyzing, and visualizing text data. Simply put, data visualization helps users the individuals or teams who generate the data, and in many cases, their audience make sense of data and make the best data. It is a storytelling tool that provides a way to communicate the meaning behind a data set. From animations to maps to other advanced graphic options (check out shiny to make interactive plots!), these extension packages help make publication-worthy graphs. Data visualization is the graphical representation of information and data. It has a very intuitive design, and is very easy to learn for beginners. If you’re trying to add to your data visualization toolkit to get a new job, this is one of the best to learn. There are also numerous packages meant to extend the functionality of ggplot2. Tableau is the biggest data visualization tool out there, with almost 60,000 customers worldwide. The ggplot2package is the primary graphic-making package. R is not only a standard statistical analysis tool, but also a powerful visualization platform. The individual has the skills to use different Python Libraries, mainly Matplotlib and Seaborn to generate different types of visualization tools such as line. If you are working with a scripting language for other aspects of data analysis, you're in luck! You can often use the same software for everything from data cleaning to data visualization for both numeric and text data.
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