Test Driven Data Analysis
What is it?
The TDDA Python module provides command-line and Python API support for the overall process of data analysis, through the following tools:
- Reference Testing: extensions to unittest and pytest for managing testing of data analysis pipelines, where the results are typically much larger, and more complex, than single numerical values.
- Constraints: tools (and API) for discovery of constraints from data, for validation of constraints on new data, and for anomaly detection.
- Finding Regular Expressions: tools (and API) for automatically inferring regular expressions from text data.
The simplest way to install all of the TDDA Python modules is using pip:
pip install tdda
The full set of sources, including all examples, are downloadable from PyPi with:
pip download –no-binary :all: tdda
The sources are also publicly available from Github:
git clone firstname.lastname@example.org:tdda/tdda.git
Documentation is available at http://tdda.readthedocs.io.
If you clone the Github repo, use
python setup.py install
afterwards to install the command-line tools (tdda and rexpy).
The tdda.referencetest library is used to support the creation of reference tests, based on either unittest or pytest.
These are like other tests except:
- They have special support for comparing strings to files and files to files.
- That support includes the ability to provide exclusion patterns (for things like dates and versions that might be in the output).
- When a string/file assertion fails, it spits out the command you need to diff the output.
- If there were exclusion patterns, it also writes modified versions of both the actual and expected output and also prints the diff command needed to compare those.
- They have special support for handling CSV files.
- It supports flags (-w and -W) to rewrite the reference (expected) results once you have confirmed that the new actuals are correct.
For more details from a source distribution or checkout, see the README.md file and examples in the referencetest subdirectory.
The tdda.constraints library is used to ‘discover’ constraints from a (Pandas) DataFrame, write them out as JSON, and to verify that datasets meet the constraints in the constraints file.
For more details from a source distribution or checkout, see the README.md file and examples in the constraints subdirectory.
Finding Regular Expressions
The tdda repository also includes rexpy, a tool for automatically inferring regular expressions from a single field of data examples.
Resources on these topics include:
- TDDA Blog: http//www.tdda.info
- Quick Reference Guide (“Cheatsheet”): http://www.tdda.info/pdf/tdda-quickref.pdf
- Full documentation: http://tdda.readthedocs.io
- General Notes on Constraints and Assertions: http://www.tdda.info/constraints-and-assertions
- Notes on using the Pandas constraints library: http://www.tdda.info/constraint-discovery-and-verification-for-pandas-dataframes
- PyCon UK Talk on TDDA:
- Video: https://www.youtube.com/watch?v=FIw_7aUuY50
- Slides and Rough Transcript: http://www.tdda.info/slides-and-rough-transcript-of-tdda-talk-from-pycon-uk-2016
All examples, tests and code run under Python 2.7, Python 3.5 and Python 3.6.
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|tdda-1.0.7-py2-none-any.whl (246.1 kB) Copy SHA256 hash SHA256||Wheel||2.7||May 31, 2018|
|tdda-1.0.7-py3-none-any.whl (246.1 kB) Copy SHA256 hash SHA256||Wheel||3.5||May 31, 2018|
|tdda-1.0.7.tar.gz (192.5 kB) Copy SHA256 hash SHA256||Source||None||May 31, 2018|