Posted on Thu 24 October 2019 in TDDA • Tagged with tdda, python, installation

This post is a standing post that we plan to try to keep up to date, describing options for obtaining the open-source Python TDDA library that we maintain.

Using pip from PyPI

If you don't need source, and have Python installed, the easiest way to get the TDDA library is …

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Rexpy for Generating Regular Expressions: Postcodes

Posted on Wed 20 February 2019 in TDDA • Tagged with regular expressions, rexpy, tdda

Rexpy is a powerful tool we created that generates regular expressions from examples. It's available online at and forms part of our open-source TDDA library.

Miró users can use the built-in rex command.

This post illustrates using Rexpy to find regular expressions for UK postcodes.

A …

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Our Approach to Data Provenance

Posted on Tue 12 December 2017 in TDDA • Tagged with data lineage, data provenance, data governance, tdda, constraints, miro

NEW DATA GOVERNANCE RULES: — We need to track data provenance. — No problem! We do that already! — We do? — We do! — (thinks) Results2017_final_FINAL3-revised.xlsx

Our previous post introduced the idea of data provenance (a.k.a. data lineage), which has been discussed on a couple of podcasts recently. This is an issue that is close to our hearts at Stochastic Solutions. Here, we'll talk about how we handle this issue, both methodologically and in …

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Data Provenance and Data Lineage: the View from the Podcasts

Posted on Thu 30 November 2017 in TDDA • Tagged with data lineage, data provenance, data governance, tdda, constraints

In Episode 49 of the Not So Standard Deviations podcast, the final segment (starting at 59:32) discusses data lineage, after Roger Peng listened to the September 3rd (2017) episode of another podcast, Linear Digressions, which discussed that subject.

This is a topic very close to our hearts, and I …

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Automatic Constraint Generation and Verification White Paper

Posted on Fri 06 October 2017 in TDDA • Tagged with tdda, constraints, verification, bad data

We have a new White Paper available:

Automatic Constraint Generation and Verification


Correctness is a key problem at every stage of data science projects: completing an entire analysis without a serious error at some stage is surprisingly hard. Even errors that reverse or completely invalidate the analysis can be …

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