Introduction A surprisingly tricky problem in doing data science or analytics in the database are situations where one has to re-map a large number of columns. This occurs, for example, in the vtreat data preparation system. In the vtreat case, a large number of the variable encodings reduce to table-lookup […]

Estimated reading time: 15 minutes

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 […]

Estimated reading time: 8 minutes

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 […]

Estimated reading time: 8 minutes

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 […]

Estimated reading time: 1 minute

I’ve been writing a lot about a category theory interpretations of data-processing pipelines and some of the improvements we feel it is driving in both the data_algebra and in rquery/rqdatatable. I think I’ve found an even better category theory re-formulation of the package, which I will describe here.

Estimated reading time: 12 minutes

In our recent note What is new for rquery December 2019 we mentioned an ugly processing pipeline that translates into SQL of varying size/quality depending on the query generator we use. In this note we try a near-relative of that query in the data_algebra.

Estimated reading time: 2 minutes

This note is a simple data wrangling example worked using both the Python data_algebra package and the R cdata package. Both of these packages make data wrangling easy through he use of coordinatized data concepts (relying heavily on Codd’s “rule of access”). The advantages of data_algebra and cdata are: The […]

Estimated reading time: 17 minutes

To make getting started with rquery (an advanced query generator for R) easier we have re-worked the package README for various data-sources (including SparkR!).

Estimated reading time: 39 seconds

My BARUG rquery talk went very well, thank you very much to the attendees for being an attentive and generous audience. (John teaching rquery at BARUG, photo credit: Timothy Liu) I am now looking for invitations to give a streamlined version of this talk privately to groups using R who […]

Estimated reading time: 1 minute

I would like to thank LinkedIn for letting me speak with some of their data scientists and analysts. John Mount discussing rquery SQL generation at LinkedIn. If you have a group using R at database or Spark scale, please reach out ( jmount at win-vector.com ). We (Win-Vector LLC) have […]

Estimated reading time: 49 seconds