This repository contains the slides, exercises, and answers for Parts I and II of Programming with Data: Python and Pandas.

The easiest way to view the slides and try the exercises is to click the Binder badge above.

You may view the slides and the answers directly in your browser on Github though you will not be able to run them interactively.

If you're taking the course, want to follow along with the slides and do the exercises, and may not have Internet access, download and install the Anaconda Python 3 distribution and conda package manager ahead of time:

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

Download the latest version of the course materials here.

Alternatively, you may clone the course repository using git:

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

The remainder of the installation requires that you use the command line.

To complete the course 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: conda activate pydata or source activate pydata

Once you've activated the environment your prompt will probably look something like this:

(pydata) $

The entire course is designed to use jupyter notebooks. Start the notebook server to get started:

(pydata) $ jupyter notebook

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.

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