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width=\"120\"/\u003e\n\n[![PyPI](https://img.shields.io/pypi/v/mlqa)](https://pypi.org/project/mlqa/)\n[![tests](https://github.com/ddaskan/mlqa/workflows/tests/badge.svg)](https://github.com/ddaskan/mlqa/actions?query=workflow%3Atests)\n[![Codecov](https://codecov.io/gh/ddaskan/mlqa/master.svg)](https://codecov.io/gh/ddaskan/mlqa/)\n[![Documentation Status](https://readthedocs.org/projects/mlqa/badge/?version=latest)](https://mlqa.readthedocs.io/en/latest/?badge=latest)\n[![PyPI - Downloads](https://img.shields.io/pypi/dm/mlqa)](https://pypi.org/project/mlqa/)\n[![GitHub last commit](https://img.shields.io/github/last-commit/ddaskan/mlqa)](https://github.com/ddaskan/mlqa)\n[![Twitter](https://img.shields.io/twitter/url?style=social\u0026url=https%3A%2F%2Fgithub.com%2Fddaskan%2Fmlqa)](https://twitter.com/intent/tweet?text=Wow:\u0026url=https%3A%2F%2Fgithub.com%2Fddaskan%2Fmlqa)\n\nA Package to perform QA on data flows for Machine Learning.\n\n## Introduction\n\nMLQA is a Python package that is created to help data scientists, analysts and developers to perform quality assurance (i.e. QA) on [pandas dataframes](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) and 1d arrays, especially for machine learning modeling data flows. It's designed to work with [logging](https://docs.python.org/3/library/logging.html) library to log and notify QA steps in a descriptive way. It includes stand alone functions (i.e. [checkers](mlqa/checkers.py)) for different QA activities and [DiffChecker](mlqa/identifiers.py) class for integrated QA capabilities on data.\n\n## Installation\n\nYou can install MLQA with pip.\n\n`pip install mlqa`\n\nMLQA depends on Pandas and Numpy and works in Python 3.7+.\n\n## Quickstart\n\n[DiffChecker](mlqa/identifiers.py) is designed to perform QA on data flows for ML. You can easily save statistics from the origin data such as missing value rate, mean, min/max, percentile, outliers, etc., then to compare against the new data. This is especially important if you want to keep the prediction data under the same assumptions with the training data.\n\nBelow is a quick example on how it works, just initiate and save statistics from the input data.\n```python\n\u003e\u003e\u003e from mlqa.identifiers import DiffChecker\n\u003e\u003e\u003e import pandas as pd\n\u003e\u003e\u003e dc = DiffChecker()\n\u003e\u003e\u003e dc.fit(pd.DataFrame({'mean_col':[1, 2]*50, 'na_col':[None]*50+[1]*50}))\n\n```\n\nThen, you can check on new data if it's okay for given criteria. Below, you can see some data that is very similar in column `mean_col` but increased NA count in column `na_col`. The default threshold is 0.5 which means it should be okay if NA rate is 50% more than the origin data. NA rate is 50% in the origin data so up to 75% (i.e. 50*(1+0.5)) should be okay. NA rate is 70% in the new data and, as expected, the QA passes.\n\n```python\n\u003e\u003e\u003e dc.check(pd.DataFrame({'mean_col':[.99, 2.1]*50, 'na_col':[None]*70+[1]*30}))\nTrue\n\n```\n\nSee more examples at [Documentation/Quickstart](https://mlqa.readthedocs.io/en/latest/source/quickstart.html). You can also read the full documentation [here](https://mlqa.readthedocs.io/).\n\n## Tests\nTests are written with [unittest](https://docs.python.org/3/library/unittest.html) and can be located in the [tests](tests/) folder. There are also some tests in docstring to be run by [doctest](https://docs.python.org/3/library/doctest.html).\n\n## License\n[MIT](https://github.com/ddaskan/mlqa/blob/master/LICENSE)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fddaskan%2Fmlqa","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fddaskan%2Fmlqa","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fddaskan%2Fmlqa/lists"}