Analyzing Big Data with Python PANDAS
This is a series of iPython notebooks for analyzing Big Data — specifically Twitter data — using Python’s powerful PANDAS (Python Data Analysis) library. Through these tutorials I’ll walk you through how to analyze your raw social media data using a typical social science approach.
The target audience is those who are interested in covering key steps involved in taking a social media dataset and moving it through the stages needed to deliver a valuable research product. I’ll show you how to import your data, aggregate tweets by organization and by time, how to analyze hashtags, how to create new variables, how to produce a summary statistics table for publication, how to analyze audience reaction (e.g., # of retweets) and, finally, how to run a logistic regression to test your hypotheses. Collectively, these tutorials cover essential steps needed to move from the data collection to the research product stage.
I’ve put these tutorials in a GitHub repository called PANDAS. For these tutorials I am assuming you have already downloaded some data and are now ready to begin examining it. In the first notebook I will show you how to set up your ipython working environment and import the Twitter data we have downloaded. If you are new to Python, you may wish to go through a series of tutorials I have created in order.
If you want to skip the data download and just use the sample data, but don’t yet have Python set up on your computer, you may wish to go through the tutorial “Setting up Your Computer to Use My Python Code”.
As you may know from my other tutorials, I am a big fan of the free Anaconda version of Python 2.7. It contains all of the prerequisites you need and will save you a lot of headaches getting your system set up.
At the GitHub site you’ll find the following chapters in the tutorial set:
Chapter 1 – Import Data, Select Cases and Variables, Save DataFrame.ipynb
Chapter 2 – Aggregating and Analyzing Data by Twitter Account.ipynb
Chapter 3 – Analyzing Twitter Data by Time Period.ipynb
Chapter 4 – Analyzing Hashtags.ipynb
Chapter 5 – Generating New Variables.ipynb
Chapter 6 – Producing a Summary Statistics Table for Publication.ipynb
Chapter 7 – Analyzing Audience Reaction on Twitter.ipynb
Chapter 8 – Running, Interpreting, and Outputting Logistic Regression.ipynb
I hope you find these tutorials helpful; please acknowledge the source in your own research papers if you’ve found them useful:
Saxton, Gregory D. (2015). Analyzing Big Data with Python. Buffalo, NY: http://social-metrics.org
Also, please share and spread the word to help build a vibrant community of PANDAS users.