Data Visualization with Python

In today’s world, a lot of data is being generated on a daily basis and sometimes to analyzing this data for certain trends, patterns may become difficult sometimes if the data is in its raw form or in tabular form. Data analysts/Data scientists may gather all the best, most useful information in existence, but if the clients and users can’t understand it, it is useless. So, the data must be presented in easy-to-use formats so that an average layperson can understand what it is indicating. To overcome this is where data visualization comes in handy to rescue. As the old saying goes, “a picture is worth a thousand words”. Data visualization helps paint that picture, which in turn fosters greater understanding. Data visualization provides a good, organized pictorial representation of the data which makes it easier to understand, observe, and analyze. Data visualization helps in understanding what exactly data is conveying, and to better clean it and select suitable modelsModel for it. Data visualization helps expose patterns, correlationsCorrelation, and trends that cannot be understood obtained when data is in a tabular format. Using data visualization, one can get a visual summary of the data. With pictures, maps, and graphs, the human mind finds it has an easier to time processing and understanding the given data. Data visualization plays an important role in the representation of both small and large datasets, but it is especially useful when we have large datasets, in which it is impossible to see all of our data, let alone process and understand it manually. In this chapter, we will discuss how to visualize data using Python.