pandas is useful for a lot of ancillary data activities, such as exploratory data analysis, validating the sanctity (such as the data type or count) of data between two data sources, and structuring and shaping data obtained from another source, such as scraping a website or a database. In this chapter, we dealt with some case studies on these topics. A data scientist performs these activities on a day-to-day basis, and this chapter should give a flavor of what it is like to perform them on a real dataset.
In the next chapter, we will discuss the architecture and code structure of the pandas library. This will help us develop an exhaustive understanding of the functionalities of the library and enable us to do better troubleshooting.
