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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

Unpack the file (replacing X.Y.Z with the appropriate version number) and review the source code:

.. code-block:: console

tar xvfz datatest-X.Y.Z.tar.gz

Change to the unpacked directory and run the tests:

.. code-block:: console

cd datatest-X.Y.Z
python setup.py test

Don't worry if some of the tests are skipped. Tests for optional data sources (like pandas DataFrames or NumPy arrays) are skipped when the related third-party packages are not installed.

If the source code and test results are satisfactory, install the package:

.. code-block:: console

python setup.py install

.. end-inclusion-marker-install

Supported Versions

Tested on Python 2.6, 2.7, 3.2 through 3.10, PyPy, and PyPy3. Datatest is pure Python and may also run on other implementations as well (check using "setup.py test" before installing).

Backward Compatibility

If you have existing tests that use API features which have changed since 0.9.0, you can still run your old code by adding the following import to the beginning of each file:

.. code-block:: python

from datatest.__past__ import api09

To maintain existing test code, this project makes a best-effort attempt to provide backward compatibility support for older features. The API will be improved in the future but only in measured and sustainable ways.

All of the data used at the National Committee for an Effective Congress <http://www.ncec.org/about>_ has been checked with datatest for several years so there is, already, a large and growing codebase that relies on current features and must be maintained into the future.

Soft Dependencies

Datatest has no hard, third-party dependencies. But if you want to interface with pandas DataFrames, NumPy arrays, or other optional data sources, you will need to install the relevant packages (pandas, numpy, etc.).

Development Repository

The development repository for datatest is hosted on GitHub <https://github.com/shawnbrown/datatest>_.

Freely licensed under the Apache License, Version 2.0

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