Chapter 3 – Analyzing Twitter Data by Time Period
Chapter 2 – Aggregating and Analyzing Data by Twitter Account
Chapter 1 – Import Data, Select Cases and Variables, Save DataFrame
Tutorials for Sarbanes-Oxley Paper Data
Dan Neely (from University of Milwaukee-Wisconsin) and I just had the following article published at the Journal of Business Ethics: Saxton, G. D., & Neely, D. G. (2018). The Relationship Between Sarbanes–Oxley Policies and Donor Advisories in Nonprofit Organizations. Journal of Business Ethics. This page contains tutorials on how to download the IRS 990 e-file […]
Python PANDAS Code Bytes
This page contains brief (generally one-liner) blocks of code for working with Python and PANDAS for data analytics. I created it as a handy reference for PANDAS commands I tended to forget when I was learning. I hope it proves useful to you, too! I also have a page with longer data analytics tutorials. Table […]
Using Your Twitter API Key
Below is an embedded version of an iPython notebook I have made publicly available on nbviewer. To download a copy of the code, click on the icon with three horizontal lines at the top right of the notebook (just below this paragraph) and select “Download Notebook.” I hope you find it helpful. If so, please […]
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 […]
Producing a Summary Statistics Table in iPython using PANDAS
Below is an embedded version of an iPython notebook I have made publicly available on nbviewer. To download a copy of the code, click on the icon with three horizontal lines at the top right of the notebook (just below this paragraph) and select “Download Notebook.” I hope you find it helpful. If so, please […]
iPython Notebook and PANDAS Cookbook
More and more of my research involves some degree of ‘Big Data’ — typically datasets with a million or so tweets. Getting these data prepped for analysis can involve massive amounts of data manipulation — anything from aggregating data to the daily or organizational level, to merging in additional variables, to generating data required for […]
