This exercise is part of the course
In this chapter, you'll learn how to import data into Python from all types of flat files, a simple and prevalent form of data storage. You've previously learned how to use NumPy and Pandas - you will learn how to use these packages to import flat files, as well as how to customize your imports.
You've learned how to import flat files, but there are many other file types you will potentially have to work with as a data scientist. In this chapter, you'll learn how to import data into Python from a wide array of important file types. You will be importing file types such as pickled files, Excel spreadsheets, SAS and Stata files, HDF5 files, a file type for storing large quantities of numerical data, and MATLAB files.
In this chapter, you'll learn how to extract meaningful data from relational databases, an essential element of any data scientist's toolkit. You will be learning about the relational model, creating SQL queries, filtering and ordering your SQL records, and advanced querying by JOINing database tables.
Current Exercise
The web is a rich source of data from which you can extract various types of insights and findings. In this chapter, you will learn how to get data from the web, whether it be stored in files or in HTML. You'll also learn the basics of scraping and parsing web data.
In this chapter, you will push further on your knowledge of importing data from the web. You will learn the basics of extracting data from APIs, gain insight on the importance of APIs and practice getting data from them with dives into the OMDB, Wikipedia and Twitter APIs.