The data algebra is a system for specifying data transformations in Pandas or SQL databases. To use it, we advise checking out the README and introduction. These document what data operators are the basis of data algebra transformation construction and composition. I have now added a catalog of what expression […]
Machine learning “in the database” (including systems such as Spark) is an increasingly popular topic. And where there is machine learning, there is a need for data preparation. Many machine learning algorithms expect all data to be numeric without missing values. vtreat is a package (available for Python or for […]
When working with multiple data tables we often need to know how for a given set of keys, how many instances of rows each table has. I would like to use such an example in Python as yet another introduction to the data algebra (an alternative to direct Pandas or […]
For no good reason I decided to work out what shape minimized the tension at the attachment points of a draped cable. It turns out to be a lot droopier than one might expect. All of the details of the calculation using sympy can be found here.
I’d like to write a bit about measuring effect sizes and Cohen’s d. Introduction For our note let’s settle on a single simple example problem. We have two samples of real numbers a_1, …, a_n and b_1, …, b_n. All the a_i are mutually exchangeable or generated by an independent […]
I am pleased to announce the 0.9.0 release of the data algebra. The data algebra is realization of the Codd relational algebra for data in written in terms of Python method chaining. It allows the concise clear specification of useful data transforms. Some examples can be found here. Benefits include […]
I would like to share another quick tutorial on some aspects of the data algebra, this time using the example of comparing two tables. Please check it out here.
I have a new intermediate introduction on the data algebra up here: Using the data algebra for Statistics and Data Science. The data algebra is a tool for data processing in Python which is implemented on top of any of Pandas, Google BigQuery, PostgreSQL, MySQL, Spark, and SQLite. It allows […]
I’ve thought of Pandas as in-memory column oriented data structure with reasonable performance. If I need high performance or scale, I can move to a database. I like Pandas, and thank the authors and maintainers for their efforts. Now I kind of wonder what Pandas is, or what it wants […]
Back to teaching. For a few years we’ve been running a data science intensive at for a really neat FAAMG company. The idea is to give engineers some hands on live workbook time using methods varying from linear regression, xgboost, to deep neural networks. Learning how participants progress and internalize […]