Python Tutorial : Importing flat files from the web

Want to learn more? Take the full course at https://learn.datacamp.com/courses/intermediate-importing-data-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- You're now able to import data in Python from all sorts of file types: flat files such as dot txt's and dot csv's, other file types such as pickled files, Excel spreadsheets and MATLAB files. You've also gained valuable experience in querying relational databases to import data from them using SQL. You have really come a long way, congratulations! However, all of these skills involve importing data from files that you have locally. Much of the time as a data scientist, these skills won't be quite enough because you won't always have the data that you need. You will need to import it from the web. Say, for example, you want to import the Wine Quality dataset from the Machine Learning Repository hosted by the University of California, Irvine. How do you get this file from the web? Now you could use your favourite web browser of choice to navigate to the relevant URL, point and click on the appropriate hyperlinks to download the file but this poses a few problems. Firstly, it isn't written in code and so poses reproducibility problems. If another Data Scientist wanted to reproduce your workflow it, she would necessarily have to do so outside Python. Secondly, it is NOT scalable. If you wanted to download one hundred or one thousand such files, it would take one hundred or one thousand times as long, respectively, whereas if you wrote it in code, your workflow could scale. As reproducibility and scalability are situated at the very heart of Data Science, you're going to learn in this chapter how to use Python code to import and locally save datasets from the world wide web. You'll also learn how to load such datasets into pandas dataframes directly from the web, whether they be flat files or otherwise. Then you'll place these skills in th