Data are everywhere. Many government agencies, financial institutions, universities, and social media platforms provide access to their data through an application programming interface (API). APIs often return the requested data in a JavaScript Object Notation (JSON) file. In this post, I will show you how to use Python to request data with API calls and how to work with the resulting JSON data. Read more…
In my first four posts about Stata and Python, I showed you how to set up Stata to use Python, three ways to use Python in Stata, how to install Python packages, and how to use Python packages. It might be helpful to read those posts before you continue with this post if you are not familiar with Python. Now, I’d like to shift our focus to some practical uses of Python within Stata. This post will demonstrate how to use Stata to estimate marginal predictions from a logistic regression model and use Python to create a three-dimensional surface plot of those predictions.
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In my last post, I showed you how to use pip to install four popular packages for Python. Today I want to show you the basics of how to import and use Python packages. We will learn some important Python concepts and jargon along the way. I will be using the pandas package in the examples below, but the ideas and syntax are the same for other Python packages. Read more…
In my last post, I showed you three ways to use Python within Stata. The examples were simple but they allowed us to start using Python. At this point, you could write your own Python programs within Stata. But the real power of Python lies in the thousands of freely available packages. Today, I want to show you how to download and install Python packages. Read more…
