Posted on Categories data science, Statistics, TutorialsTags , , , , , Leave a comment on New improved cdata instructional video

New improved cdata instructional video

We have a new improved version of the “how to design a cdata/data_algebra data transform” up!

The original article, the Python example, and the R example have all been updated to use the new video.

Please check it out!

Posted on Categories data science, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , , , , , , Leave a comment on Data re-Shaping in R and in Python

Data re-Shaping in R and in Python

Nina Zumel and I have a two new tutorials on fluid data wrangling/shaping. They are written in a parallel structure, with the R version of the tutorial being almost identical to the Python version of the tutorial.

This reflects our opinion on the “which is better for data science R or Python?” They both are great. So start with one, and expect to eventually work with both (if you are lucky).

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Posted on Categories Exciting Techniques, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , , Leave a comment on sklearn Pipe Step Interface for vtreat

sklearn Pipe Step Interface for vtreat

We’ve been experimenting with this for a while, and the next R vtreat package will have a back-port of the Python vtreat package sklearn pipe step interface (in addition to the standard R interface).

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Posted on Categories data science, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , , Leave a comment on New vtreat Feature: Nested Model Bias Warning

New vtreat Feature: Nested Model Bias Warning

For quite a while we have been teaching estimating variable re-encodings on the exact same data they are later naively using to train a model on, leads to an undesirable nested model bias. The vtreat package (both the R version and Python version) both incorporate a cross-frame method that allows one to use all the training data both to build learn variable re-encodings and to correctly train a subsequent model (for an example please see our recent PyData LA talk).

The next version of vtreat will warn the user if they have improperly used the same data for both vtreat impact code inference and downstream modeling. So in addition to us warning you not to do this, the package now also checks and warns against this situation. vtreat has had methods for avoiding nested model bias for vary long time, we are now adding new warnings to confirm users are using them.

Set up the Example

This example is excerpted from some of our classification documentation.

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Posted on Categories data science, Opinion, Pragmatic Data Science, TutorialsTags , , , , , , , , , 1 Comment on New Timings for a Grouped In-Place Aggregation Task

New Timings for a Grouped In-Place Aggregation Task

I’d like to share some new timings on a grouped in-place aggregation task. A client of mine was seeing some slow performance, so I decided to time a very simple abstraction of one of the steps of their workflow.

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Posted on Categories data science, Pragmatic Data Science, TutorialsTags , , , ,

A Richer Category for Data Wrangling

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.

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Posted on Categories Administrativia, Computer Science, Pragmatic Data ScienceTags , , , ,

Better SQL Generation via the data_algebra

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.

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Posted on Categories data science, TutorialsTags , , , , 1 Comment on data_algebra/rquery as a Category Over Table Descriptions

data_algebra/rquery as a Category Over Table Descriptions

Introduction

I would like to talk about some of the design principles underlying the data_algebra package (and also in its sibling rquery package).

The data_algebra package is a query generator that can act on either Pandas data frames or on SQL tables. This is discussed on the project site and the examples directory. In this note we will set up some technical terminology that will allow us to discuss some of the underlying design decisions. These are things that when they are done well, the user doesn’t have to think much about. Discussing such design decisions at length can obscure some of their charm, but we would like to point out some features here.

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Posted on Categories Administrativia, data science, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, StatisticsTags , , ,

Slides for PyData LA 2019 vtreat Talk

Slides for PyData LA 2019 vtreat Talk are here!