The Top 5 Data Science Libraries

There are several articles detailing beneficial Data Science libraries, as well as packages, platforms, and modules, so I am going to do my best in choosing not only the top libraries, but also ones that are unique in order to reduce redundancies. As a professional Data Scientist, I have not only heard that the data part of the process consumes up a lot of your time in everyday work, but I have also experienced it. Some of the libraries I will discuss will incorporate that in mind, like pandas_profiling. Additionally, I have worked not just with numeric data, but also with text data, which requires a lot of preprocessing and can be helped by libraries like nltk, textblob, and pyldavis. Lastly, some of these libraries work well as visualizations tools as well like networkx.