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Importing Data in Python (Part 2)

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  • 8 Videos
  • 30 Exercises
  • 2 hours 
  • 2,428 Participants
  • 2450 XP

Instructor(s):

Hugo Bowne-Anderson
Hugo Bowne-Anderson

Hugo hearts all things Pythonic and is charged with building out DataCamp’s Python curriculum. He can be found at hackathons, meetups & code sprints, primarily in NYC. Before joining the ranks of DataCamp, he worked in applied mathematics (biology) research at Yale University.

Collaborator(s):

Francisco Castro Francisco Castro

Course Description

As a Data Scientist, on a daily basis you will need to clean data, wrangle and munge it, visualize it, build predictive models and interpret these models. Before doing any of these, however, you will need to know how to get data into Python. In the prequel to this course, you have already learnt many ways to import data into Python: (i) from flat files such as .txts and .csvs; (ii) from files native to other software such as Excel spreadsheets, Stata, SAS and MATLAB files; (iii) from relational databases such as SQLite & PostgreSQL. In this course, you'll extend this knowledge base by learning to import data (i) from the web and (ii) a special and essential case of this: pulling data from Application Programming Interfaces, also known as APIs, such as the Twitter streaming API, which allows us to stream real-time tweets.

Interacting with APIs to import data from the web 

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 and Library of Congress APIs.

Diving deep into the Twitter API 

In this chapter, you will consolidate your knowledge of interacting with APIs in a deep dive into the Twitter streaming API. You'll learn how to stream real-time Twitter data and to analyze and visualize it!