README.md

Python APIs and Datastores

Lesson Goals

• Empowerment
  • In Soviet Russia, your data is in charge of you
  • With Python, you are in charge of your data
• Be comfortable asking questions about how to access your data
• Give a broad survey of what is possible and enough concepts so you can dig into whatever might be applicable to your environment
• Think of software development in three stages: get it working, get it working right, get it working quickly
  • Here, we focus on making it possible to get it working by providing samples of different methods of data access
  • Learn concepts that enable asking more informed questions when you undertake a data project
• Awareness that A beginning is a very delicate time - Dune, Frank Herbert
  • Two phases of a software process to consider: development and operations
  • How you think about accessing your data influences the rest of your project
  • Be comfortable as you develop, but don't forget you might need to live with what you wrote for a while

What is a Datastore?

• Where data is stored, organized, and persisted... what does this mean?
• Method for abstracting storage details from your application
  • lots of different concerns/use cases result in lots of different ways to read and write data
• Does this matter? yes and no... if you're reading data, there are some things you might care about:
  • Where it comes from
  • What its shape is
  • Performance/storage

Datastore Examples

• Files (stored in formats - e.g. CSV, XML, JSON, binary, etc. - see below)
• SQL databases
• No-SQL databases
• API

What is an API?

• API == Application Programming Interface
• A way for a program (application) to talk to another program programmatically

Types of APIs

• Memory based (e.g programs importing logic/capabilities from other programs)
  • importing libraries
  • talking to the operating system
• Network/protocols (difference between protocol and API... we'll pretend there are none for the sake of this discussion, but I'm happy to discuss more offline)
  • standard protocols (e.g. HTTP, ODBC, FTP, etc.)
• Custom formats accessed via networking protocols
  • typically data you might get over the Internet for a certain dataset or website, tailored for accessing that content
  • this brings us to data formats (which applies to files as well)
• Web pages (plug for web scraping)

Data Formats

• Assumes you have access to your pile of data
• Formats are how you turn that pile of bytes into something intelligible
  • CSV (comma-separated values)
  • JSON (Javascript Object Notation)
  • XML (eXtensible Markup Language)
  • XLS/XLSX
  • Binary
  • Roll your own
• Based on data format, you may want some form of library to consume it

Code, please

Dependencies/Package Requirements

• pip install pandas
• pip install numpy
• pip install sqlite3
• pip install matplotlib
• pip install seaborn
• pip install requests

Sanity check

• derp.py

Source examples

• csv_examples.py
• xlsx_examples.py
• db_examples.py
• api_examples.py
• zip_examples.py

Put it all together

• datamunge.py