Summary

In this chapter, you saw how pandas supports data I/O to and from a wide variety of formats, both text and digital. You saw how pandas supports acting on multi-table databases in SQL directly from Python. You also explored the different character encodings that you may encounter in text data, as well as how to extract only the desired data columns from a more complex Excel file. Given the large amounts of data on the internet, you saw how pandas can extract tables from web pages and decode more complex web data in XML or JSON formats. You also learned how to use APIs to obtain data. In most cases, all you need is the pandas .read_xxx() and .to_xxx() methods. With what you have learned and practiced in this chapter, you are ready to handle most data sources you may encounter in your work.

Here, you've focused on getting data into and out of pandas DataFrames from a wide range of file types. In the next chapter, you will begin digging into the finer details and exploring...