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Tools for test driven data-wrangling and data validation.
| Date | Stars |
|---|---|
| 2026-07-24 | 295 |
| 2026-07-25 | 295 |
| 2026-07-28 | 295 |
| 2026-07-30 | 295 |
| 2026-08-06 | 295 |
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datatest: Test driven data-wrangling and data validation
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|repobadge| |buildbadge| |statusbadge| |stabledocsbadge| |latestdocsbadge|
Datatest helps to speed up and formalize data-wrangling and data
validation tasks. It implements a system of validation methods,
difference classes, and acceptance managers. Datatest can help you:
* Clean and wrangle data faster and more accurately.
* Maintain a record of checks and decisions regarding important data sets.
* Distinguish between ideal criteria and acceptible deviation.
* Validate the input and output of data pipeline components.
* Measure progress of data preparation tasks.
* On-board new team members with an explicit and structured process.
Datatest can be used directly in your own projects or as part of a testing
framework like pytest_ or unittest_. It has no hard dependencies; it's
tested on Python 2.6, 2.7, 3.2 through 3.10, PyPy, and PyPy3; and is freely
available under the Apache License, version 2.
.. _pytest: https://pytest.org
.. _unittest: https://docs.python.org/library/unittest.html
:Documentation:
| https://datatest.readthedocs.io/ (stable)
| https://datatest.readthedocs.io/en/latest/ (latest)
:Official:
| https://pypi.org/project/datatest/
Code Examples
=============
Validating a Dictionary of Lists
--------------------------------
.. code-block:: python
from datatest import validate, accepted, Invalid
data = {
'A': [1, 2, 3, 4],
'B': ['x', 'y', 'x', 'x'],
'C': ['foo', 'bar', 'baz', 'EMPTY']
}
validate(data.keys(), {'A', 'B', 'C'})
validate(data['A'], int)
validate(data['B'], {'x', 'y'})
with accepted(Invalid('EMPTY')):
validate(data['C'], str.islower)
Validating a Pandas DataFrame
-----------------------------
.. code-block:: python
import pandas as pd
from datatest import register_accessors, accepted, Invalid
register_accessors()
df = pd.read_csv('data.csv')
df.columns.validate({'A', 'B', 'C'})
df['A'].validate(int)
df['B'].validate({'x', 'y'})
with accepted(Invalid('EMPTY')):
df['C'].validate(str.islower)
Installation
============
.. start-inclusion-marker-install
The easiest way to install datatest is to use `pip <https://pip.pypa.io>`_:
.. code-block:: console
pip install datatest
If you are upgrading from version 0.11.0 or newer, use the ``--upgrade``
option:
.. code-block:: console
pip install --upgrade datatest
Upgrading From Version 0.9.6
----------------------------
If you have an existing codebase of older datatest scripts, you should
upgrade using the following steps:
* Install datatest 0.10.0 first:
.. code-block:: console
pip install --force-reinstall datatest==0.10.0
* Run your existing code and check for DeprecationWarnings.
* Update the parts of your code that use deprecated features.
* Once your code is running without DeprecationWarnings,
install the latest version of datatest:
.. code-block:: console
pip install --upgrade datatest
Stuntman Mike
-------------
If you need bug-fixes or features that are not available
in the current stable release, you can "pip install" the
development version directly from GitHub:
.. code-block:: console
pip install --upgrade https://github.com/shawnbrown/datatest/archive/master.zip
All of the usual caveats for a development install should
apply---only use this version if you can risk some instability
or if you know exactly what you're doing. While care is taken
to never break the build, it can happen.
Safety-first Clyde
------------------
If you need to review and test packages before installing, you can
install datatest manually.
Download the latest **source** distribution from the Python Package
Index (PyPI):
https://pypi.org/project/datatest/#files
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