Whether in R, MATLAB, Stata, or python, modern data analysis, for many researchers, requires some kind of programming. The preponderance of tools and specialized languages for data analysis suggests that general purpose programming languages like C and Java do not readily address the needs of data scientists; something more is needed.

In this workshop, you will learn how to accelerate your data analyses using the Python language and Pandas, a library specifically designed for interactive data analysis. Pandas is a massive library, so we will focus on its core functionality, specifically, loading, filtering, grouping, and transforming data. Having completed this workshop, you will understand the fundamentals of Pandas, be aware of common pitfalls, and be ready to perform your own analyses.

Workshop assumes that participants have intermediate-level programming ability in Python. Participants should know the difference between a dict, list, and tuple. Familiarity with control-flow (if/else/for/while) and error handling (try/catch) are required.

No statistics background is required.

Each participant should have a laptop with the Anaconda Python 3 distribution and conda package manager installed:

https://www.anaconda.com/download/

The slides and exercises that accompany the workshop are available on Github:

$ git clone https://github.com/dgerlanc/programming-with-data.git

Downloading them prior to the workshop is required as Internet access may not be available during the workshop.

To complete the workshop exercises, you must use conda to install the dependencies specified in the environment.yml file in the repository:

$ conda env create -f environment.yml

This will create an conda environment called pydata which may be "activated" with the following commands:

• Windows: activate pydata
• Linux and Mac: source activate pydata