Community Data Science Workshops (Fall 2015)/Day 2 lecture



Welcome to the Saturday lecture section of the Community Data Science Workshop Session 2! For about 140 minutes, we'll work through an introduction to web APIs using Python via both a lecture and hand-on exercises.

Resources

 * Lecture Recording/Screencast — The file is in OGV/Theora format. If you have trouble playing it, you can install the free software VLC software which runs on Mac OSX, Windows and Linux and should be able to play the video. Keep in mind that there is lots of "dead" time where folks are working on things and getting help from mentors.

Lecture Outline

 * Introduction and context


 * You can write some tools in Python now. Congratulations!
 * Today we'll learn how to find/create data sets
 * Next week we'll get into data science (asking and answering questions)


 * Outline:


 * What did we learn in Session 1?
 * What is an API?
 * How do we use one to fetch interesting datasets?
 * How do we write programs that use the internet?
 * How can we use the placebear API to fetch bear pictures?
 * Introduction to structured data (JSON)
 * How do we use APIs in general?


 * What is a (web) API?


 * API: a structured way for programs to talk to each other (aka an interface for programs)
 * Web APIs: like a website your programs can visit (you:a website::your program:a web API)


 * How do we use an API to fetch datasets?

Basic idea: your program sends a request, the API sends data back
 * Where do you direct your request? The site's API endpoint.
 * For example: Wikipedia's web API endpoint is http://en.wikipedia.org/w/api.php
 * How do I write my request? Put together a URL; it will be different for different web APIs.
 * Check the documentation, look for code samples
 * How do you send a request?
 * Python has modules you can use, like  (they make HTTP requests)
 * What do you get back?
 * Structured data (usually in the JSON format)
 * How do you understand (i.e. parse) the data?
 * The requests module can do that using the  function!


 * How do we write Python programs that make web requests?

To use APIs to build a dataset we will need:
 * all our tools from last session: variables, etc
 * the ability to open urls on the web
 * the ability to create custom URLS
 * the ability to save to files
 * the ability to understand (i.e., parse) JSON data that APIs usually give us


 * Session 1 review


 * Navigating in the terminal and using it to run programs
 * Writing Python:
 * using variables to manipulate data
 * types of data: strings, integers, lists, dictionaries
 * if statements
 * for loops
 * printing
 * importing modules, so you can use code other people have written for you!


 * New programming concepts:


 * interpolate variables into a string using format
 * requests
 * open files and write to them
 * parsing a string (turning the string into a data structure we can manipulate) using the json module


 * How do we use an API to fetch pictures of bears?

placebear.com
 * API that takes specially crafted URLs and gives appropriately sized picture of bears
 * Exploring placebear in a browser:
 * visit the API documentation
 * bears of different sizes
 * bears in greyscale or color
 * Now we write a small program to grab an arbitrary square from placebear by asking for the size on standard in (placebear_input.py)


 * Introduction to structured data (JSON, JavaScriptObjectNotation)


 * what is json: useful for more structured data
 * import json; json.loads, or, even easier, just do it directly with requests using the  function!
 * like Python (except no single quotes)
 * simple lists, dictionaries
 * can reflect more complicated data structures
 * Example file at http://mako.cc/cdsw.json
 * download it and parse it (e.g., with a program like parse_cdswjson.py)


 * Using other APIs


 * every API is different, so read the documentation!
 * If the documentation isn't helpful, search online
 * for popular APIs, there are python modules that help you make requests and parse json

Possible issues:
 * rate limiting
 * authentication
 * text encoding issues

Lecture Slides (From Fall 2014)
We don't use these slides anymore but they might be very useful if you're following along at home or interested in refreshing your memory since they cover a lot of the same material.


 * Slides (PDF) — For viewing
 * Slides (ODP Libreoffice Slides Format) — For editing and modification