1. Python Crash Course
CSC 576: Data Science

2. Today
Python Crash Course
HTML notebooks

3. IPython Robust environment for interactive and exploratory computing
Enhanced python shell designed to accelerate the wiring, testing, and debugging of Python code
Particularly useful for interactively working with data and visualizing data
HTML notebook for connecting IPython through a web browser
Infrastructure for interactive parallel and distributive computing

4. Installation
Already included with Anaconda environment

5. Python 2 and Python 3
Python community is undergoing a drawn-out transition from Python 2 to Python 3
Python 3 is not backwards compatible!
We will use Python 3 in this course
The Grus textbook has very similar Python 2 code
Updated Python 3 code for every example is available on the book’s website (linked from our course page under “Secondary Resources”)

6. IPython History
Began in 2001 with the goal of making a better interactive Python interpreter
Encourages an execute-explore workflow rather than the typical edit-compile-run workflow

7. IPython Basics Launch from within Anaconda
Can execute arbitrary Python statements
Output is pretty-printed a little better than the standard python interpreter
Features tab completion
Pressing tab will search the namespace for any variables matching the characters that you have typed so far

8. IPython HTML Notebook Started in 2011
Tool for interactive computing and reproducible research
JSON based .ipynb document format
Can start from within Anaconda

9. Using the Notebook
An IPython notebook is made up of a number of cells.
Each cell can contain Python code.
Execute a cell by clicking on it and pressing Shift-Enter.
Output of the cell will be displayed beneath the cell. 
Global variables are shared between cells.

10. References Python for Data Analysis, McKinney, 1st Edition
Data Science from Scratch, Grus, 1st Edition