#!/usr/bin/env python
# coding: utf-8

# # Introduction to Python
# ## Getting Started

# ## Schedule
# 
# | Date         | Session      | Title                | Description                                  |
# |--------------|--------------|----------------------|----------------------------------------------|
# | Day 1        | Lecture 1    | Intro and Setup      | *Why Python, Setup, etc*                     |
# | Day 1        | Lecture 2    | Basic Python         | *Syntax, data structures, control flow, etc*  |
# | Day 2        | Lecture 3    | Numpy + Pandas I     | *Numpy basics, series/dataframe basics*|
# | Day 2        | Lecture 4    | Pandas II            | *Joining, advanced indexing, reshaping, etc*  |
# | Day 3        | Lecture 5    | Pandas III           | *Grouping, apply, transform, etc*             |
# | Day 3        | Lecture 6    | Plotting             | *Intro to plotting in Python*                 |
# | Day 3        | Lecture 7    | Intro to Modeling    | *Intro to stats/ML models in Python*         |

# ## Course materials
# 
# ### [github.com/ihmeuw/ihme-python-course](https://github.com/ihmeuw/ihme-python-course)
# 
# First, you're going to want to get a copy of this repository onto your
# machine. Simply fire up ``git`` and clone it:
# 
# 1.  Open up a shell (e.g. ``git.exe``, ``cmd.exe``, or ``terminal.app``)
# 
# 2.  Navigate to where you'd like to save this. 
#     - We recommend ``~/repos/`` (e.g. ``C:/Users/<user>/repos/`` on Windows, ``/Users/<user>/repos/`` on Mac, or ``/home/<user>/repos/`` on Unix).
# 
# 3.  Clone this repo:
#         git clone https://github.com/ihmeuw/ihme-python-course.git
#         
# If you need help with setting up `git`, see [this page](https://help.github.com/articles/set-up-git/#setting-up-git) or simply download the repo as a [zip file](https://github.com/IHME/ihme-python-course/archive/master.zip) for now...

# ![I wrote 20 short programs in Python yesterday. It was wonderful. Perl, I'm leaving you.](http://imgs.xkcd.com/comics/python.png)
# 
# via [xkcd](https://xkcd.com/353/) (see also [xkcd in python](https://pypi.python.org/pypi/xkcd/) and [xkcd for matplotlib](http://matplotlib.org/xkcd/examples/showcase/xkcd.html))

# # What is Python? 🐍
# 
# [Python](http://www.python.org/) is a widely used high-level, general-purpose, interpreted, dynamic programming language.
# 
# ## Broadly:
# 
# Officially, Python is an interpreted scripting language (meaning that it is not compiled until it is run) for the C programming language; in fact, Python itself is coded in C (though there are other non-C implementations). It offers the power and flexibility of lower level (*i.e.* compiled) languages, without the steep learning curve and associated programming overhead. The language is very clean and readable, and it is available for almost every modern computing platform.

# ## Advantages:
# 
# * Ease of programming, minimizing the time required to develop, debug and maintain code
# * Well-designed language that encourages good programming practices:
#     * Modular and object-oriented programming, good system for packaging and re-use of code
#     * Documentation tightly integrated with the code
# * A large standard library with many extensions
# 
# ## Disadvantages:
# 
# * Since Python is an interpreted and dynamically typed programming language, the execution of python code can be slow compared to compiled statically typed programming languages, such as C and Fortran
# * Somewhat decentralized, with different environments, packages and documentation spread out at different places

# # Scientific Computing in Python
# 
# 
# ## Why do we use Python at IHME?
# 
# - Powerful and easy to use
# - Interactive
# - Extensible
# - Large third-party library ecosystem
# - Free and open

# ## Powerful and easy to use
# 
# - Python is simultaneously powerful, flexible and easy to learn and use (in general, these qualities are traded off for a given programming language)
# - Anything that can be coded in C, FORTRAN, or Java can be done in Python, almost always in fewer (and more readable) lines of code, and with fewer debugging headaches
# - Its standard library is extremely rich, including modules for string manipulation, regular expressions, file compression, mathematics, profiling and debugging (*etc*)
# - Python is object-oriented, which is an important programming paradigm particularly well-suited to scientific programming, which allows data structures to be abstracted in a natural way

# ## Interactive 
# 
# - Python may be run interactively on the command line, in much the same way as R
# - Notebooks offer convenient prototyping, mixing code in with outputs such as graphs and direct viewing of data structures
# - Rather than compiling and running a particular program, commands may entered serially which is often useful for mathematical programming and debugging

# ## Extensible
# 
# - Often referred to as a “glue” language, meaning that it is a useful in a mixed-language environment 
#     - (such as at IHME, where we often have to combine R, Stata, C++, etc)
# - Python was designed to interact with other programming languages, both through interfaces like [rpy2](http://rpy2.bitbucket.org/) and by compiling directly into Python extensions using e.g. [Cython](http://cython.org/)
# - New interfaces coming out all the time, such as for GPU computing

# ## Large third-party library ecosystem
# 
# There are modules available for just about anything you could want to do in Python, with nearly 100,000 available on [PyPI](https://pypi.python.org/pypi) alone. Some notable packages:
# 
# - [**NumPy**](http://www.numpy.org/): Numerical Python (NumPy) is a set of extensions that provides the ability to specify and manipulate array data structures. It provides array manipulation and computational capabilities similar to those found in Matlab or Octave. 
# - [**SciPy**](http://www.scipy.org/): An open source library of scientific tools for Python, SciPy supplements the NumPy module. SciPy gathering a variety of high level science and engineering modules together as a single package. SciPy includes modules for graphics and plotting, optimization, integration, special functions, signal and image processing, genetic algorithms, ODE solvers, and others.
# - [**Matplotlib**](http://matplotlib.org/): Matplotlib is a python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. Its syntax is very similar to Matlab. 
# - [**Pandas**](http://pandas.pydata.org/): A module that provides high-performance, easy-to-use data structures and data analysis tools. In particular, the `DataFrame` class is useful for spreadsheet-like representation and mannipulation of data. Also includes high-level plotting functionality.
# - [**IPython**](https://ipython.org/): An enhanced Python shell, designed to increase the efficiency and usability of coding, testing and debugging Python. It includes both a Qt-based console and an interactive HTML notebook interface, both of which feature multiline editing, interactive plotting and syntax highlighting.

# ## Free and open
# 
# - Python is released on all platforms under an open license (Python Software Foundation License), meaning that the language and its source is freely distributable
# - Keeps costs down for scientists and universities operating under a limited budget
# - Frees programmers from licensing concerns for any software they may develop

# # Setup
# 
# ## Python 2 vs 3
# A [debate](https://wiki.python.org/moin/Python2orPython3) as old as time. Don't worry about it, just use Python 3 for now.
# 
# ## The Anaconda Distribution
# [Anaconda](https://www.continuum.io/anaconda-overview) is a suite of tools for Python (and R...) that will install everything you need to get up and running with Python

# ## Python interpreter
# 
# - Simply typing `python` at the command line will open up the standard Python interpreter:
# <img src='imgs/python.png' width='800'>
# - Seldom used interactively - but we'll use it a lot for running completed programs, e.g.
#     
#     `python my-program.py`
# 

# ## IPython
# 
# IPython is an interactive interpreter for Python that adds many user-friendly features on top of the standard `python`:
# 
# - Tab auto-completion
# - Command history (using up and down arrows)
# - In-line highlighting and editing of code
# - Object introspection
# - Automatic extraction of docstrings from Python objects like classes and functions
# 
# Start IPython by simply calling `ipython` from the command line:
# 
# <img src='imgs/ipython.png' width='800'>

# ## Jupyter
# 
# - [Jupyter notebooks](http://jupyter.org/) are HTML-based environments for IPython, R, and more, which allow you to interactively code, explore your data, and integrate documentation
# - *This* is a Jupyter notebook
# - Starting a Jupyter notebook server will launch a local webserver that you can view in your browser to create, view, and run notebooks:
#     
#         jupyter notebook
#     
# <img src='imgs/jupyter.png' width='800'>

# ## Spyder
# 
# - [Spyder](http://code.google.com/p/spyderlib/) is a MATLAB-like IDE for scientific computing with Python
# - Everything from code editing, execution and debugging is carried out in a single environment, and work on different calculations can be organized as projects in the IDE environment
# - Calling Spyder will open up a new project
# 
#         spyder
#         
# <img src='imgs/spyder.png' width='800'>
# 
# Another interesting new IDE for Python is [Rodeo](https://www.yhat.com/products/rodeo)

# ## Installation
# 
# ### The easy way
# Go to the [Anaconda download page](https://www.continuum.io/downloads) and download the installer for Python 3.5 (64-bit) and simply click through to follow the instructions
# 
# ### The fancy way
# If you'd like to setup a [Docker container with Anaconda](https://www.continuum.io/blog/developer-blog/anaconda-and-docker-better-together-reproducible-data-science), check out the [Docker setup instructions](../Docker-Instructions.rst). But be warned that it doesn't play terribly nicely with Windows 7 or 8...

# ## Slideshows
# If you'd like to be able to view these notebooks in slideshow mode, install [RISE](https://github.com/damianavila/RISE)
#     
#     conda install -c damianavila82 rise

# In[1]:


from IPython.display import Markdown, display
display(Markdown(open('./Exercise 1/README.md', 'r').read()))


# # References
# 
# - Slide materials inspired by and adapted from [Chris Fonnesbeck](https://github.com/fonnesbeck/HealthPolicyPython) and [J Robert Johansson](https://github.com/jrjohansson/scientific-python-lectures)