Stata/Python integration part 5: Three-dimensional surface plots of marginal predictions

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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Stata/Python integration part 4: How to use Python packages

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…

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Stata/Python integration part 3: How to install Python packages

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…

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Stata/Python integration part 2: Three ways to use Python in Stata

In my last post, I showed you how to install Python and set up Stata to use Python. Now, we’re ready to use Python. There are three ways to use Python within Stata: calling Python interactively, including Python code in do-files and ado-files, and executing Python script files. Each is useful in different circumstances, so I will demonstrate all three. The examples are intentionally simple and somewhat silly. I’ll show you some more complex examples in future posts, but I want to keep things simple in this post. Read more…

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Stata/Python integration part 1: Setting up Stata to use Python

Python integration is one of the most exciting features in Stata 16. There are thousands of free Python packages that you can use to access and process data from the Internet, visualize data, explore data using machine-learning algorithms, and much more. You can use these Python packages interactively within Stata or incorporate Python code into your do-files. And there are a growing number of community-contributed commands that have familiar, Stata-style syntax that use Python packages as the computational engine. But there are a few things that we must do before we can use Python in Stata. This blog post will show you how to set up Stata to use Python. Read more…

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Stata support for Apple Silicon

Apple recently announced that it will be transitioning from Intel processors to its own ARM architecture processors currently being called Apple Silicon. Stata has a long history of supporting Macs, which includes the transitions from Motorola to PowerPC processors, from MacOS Classic to MacOS X, and from PowerPC to Intel processors. We will be working to support the new Macs as they transition from Intel processors to Apple Silicon and will continue our support of Macs with Intel processors as well.

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Just released from Stata Press: Data Management Using Stata: A Practical Handbook, Second Edition

Stata Press is pleased to announce the release of Data Management Using Stata: A Practical Handbook, Second Edition by Michael N. Mitchell.

Whether you are a new user needing to import, clean, and prepare data for your first analysis in Stata or you are an experienced user hoping to learn new tricks for the most challenging tasks, this book is for you. You can jump straight to the section of the book that discusses the particular challenge you are facing. There you will find a clear explanation of how to approach the problem and illustrative examples to guide you. Read more…

Revealed preference: Stata for reproducible research

I care about reproducible research. Anyone who has ever been a research assistant or tried to follow the path set by other researchers also cares. Sometimes, reproducing others’ results is a frustrating task; sometimes, it is outright impossible. Yet sometimes, it is satisfyingly simple. In my experience, reproducing results is easy when it involves a Stata do-file. I believe this is true even beyond my personal bias (I work for Stata and used the software regularly before that). A recent article published by the American Economic Association (AEA), Vilhuber, Turrito, and Welch (2020), shows that Stata is the preferred package among economists, and I believe reproducibility is a big reason why. Read more…

How to create animated choropleth maps using the COVID-19 data from Johns Hopkins University

In my previous posts, I showed how to download the COVID-19 data from the Johns Hopkins GitHub repository, graph the data over time, and create choropleth maps. Now, I’m going to show you how to create animated choropleth maps to explore the distribution of COVID-19 over time and place.

The video below shows the cumulative number of COVID-19 cases per 100,000 population for each county in the United States from January 22, 2020, through April 5, 2020. The map doesn’t change much until mid-March, when the virus starts to spread faster. Then, we can see when and where people are being infected. You can click on the “Play” icon on the video to play it and click on the icon on the bottom right to view the video in full-screen mode.

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How to create choropleth maps using the COVID-19 data from Johns Hopkins University

In my last post, we learned how to import the raw COVID-19 data from the Johns Hopkins GitHub repository and convert the raw data to time-series data. This post will demonstrate how to download raw data and create choropleth maps like figure 1.

Figure 1: Confirmed COVID-19 cases in United States adjusted for population size

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