Introduction

Resources

These notes are a compilation of the following original resources for the purposes of the class I teach:

• Python 3 documentation
• Python Scientific Lecture Notes
• Dive into Python 3
• Python for Econometrics
• High Performance Scientific Computing
• DataJoy documentation
• Learn Python The Hard Way
• An introduction to Numpy and Scipy

Why Python?

• 10 Reasons Python Rocks for Research (And a Few Reasons it Doesn’t)
• Choosing R or Python for data analysis? An infographic
• Data Science Programming: Python vs R
• Python vs Matlab
• Infographic: Quick Guide on SAS vs R vs Python

How to start using Python

Online code editors which allow to bypass any difficulties connected with installation of Python on your own machine:

• Wakari.io (seems to be at full capacity very frequently)

For local installation it is recommended to download Anaconda distribution. Anaconda includes my favorite IDE, Spyder.

Essential libraries

• Pandas - data analysis library
• Numpy - fundamental package for scientific computing
• SciPy - numerical routines
• StatsModels - econometrics tools
• Matplotlib - plotting library
• Seaborn - pretty plotting and basic visual analysis
• Bokeh - Interactive plotting
• Plotly - Interactive plotting

Data sources

• Quandl
• Google Finance
• Yahoo Finance
• FRED