Programming with Data: Python and Pandas
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:
Download the latest version of the course materials here.
Alternatively, you may clone the course repository using
$ 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:
- Linux and Mac:
conda activate pydataor
source activate pydata
Once you've activated the environment your prompt will probably look something like this:
The entire course is designed to use
jupyter notebooks. Start the
notebook server to get started:
(pydata) $ jupyter notebook
Programming with Data
Foundations of Python and Pandas
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
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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