Test Driven Data Analysis
The simplest way to install all of the TDDA Python modules is using pip:
pip install tdda
But note that it is not particularly easy to run through the examples using a PyPi binary installation like that. The full set of sources, including all examples, will be downloadable from PyPi with:
pip download –no-binary :all: tdda
The sources are also publicly available from Github:
git clone email@example.com: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.
The package includes docstrings, available with:
>>> from tdda import referencetest >>> help(referencetest)
For more details from a source distribution or checkout, see the README.md file and examples in the referencetest subdirectory.
An older implementation of these ideas is available as a unittest wrapper class, WritableTestCase, in writabletestcase.py. This can be imported directly with from tdda.writabletestcase import WritableTestCase. Examples of using this are currently available in the deprecated subdirectory. This older version will be fully deprecated and removed soon.
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 usage details:
>>> from tdda import constraints >>> help(constraints)
For more details from a source distribution or checkout, see the README.md file and examples in the constraints subdirectory.
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 should run under Python2 and Python3.
The tdda repository also includes rexpy, a tool for automatically inferring regular expressions from a single field of data examples.
The package also has doc strings, which you can see with:
>>> from tdda import rexpy >>> help(rexpy)
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