If you visit the Project Jupyter website you’ll encounter a bunch of “try it in your browser” buttons. If you’ve used Jupyter for a decade or so like me, you probably have also been ignoring these buttons.
I’m excited to announce that the new SAPy v4.6.0 release includes a pull request of mine that adds PROC MI to the SAS/STAT procedures directly exposed in SASPy. This procedure allows you to analyze missing data patterns and create imputations for missing data.
When the endpoint of your match depends on an earlier term, try conditional regex matching in Python.
One of the editors I use regularly is VS Code. I work a lot with Python, but when installing Anaconda using default settings on a Windows machine already having VSC installed there’s a good chance you’ll run into an issue.
I have been using both SAS and Python extensively for a while now. With each having great features, it was very useful to combine my skills in both languages by seamlessly moving between SAS and Python in a single notebook.
A popular beginners machine learning problem is the prediction of housing prices. A frequently used data set for this purpose uses housing prices in California along some additional gathered through the 1990 Census.
What do you do when your data table is in PDF format? Let's use tabula-py to extract teacher salary information from PDFs directly into Pandas dataframes. We'll also use some regex to clean up the results.
The Census Bureau makes an incredible amount of data available online. In this post, I will summarize how to get access to this data via Python by using the Census Bureau’s API.