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"description": "\n\n# Pandera\n\nA flexible and expressive [pandas](http://pandas.pydata.org) validation library.\n\n
\n\n[](https://travis-ci.org/pandera-dev/pandera)\n[](https://pypi.org/project/pandera/)\n[](https://pypi.python.org/pypi/pandera/)\n[](https://www.repostatus.org/#active)\n[](https://pandera.readthedocs.io/en/latest/?badge=latest)\n[](https://codecov.io/gh/pandera-dev/pandera)\n[](https://pypi.python.org/pypi/pandera/)\n[](https://doi.org/10.5281/zenodo.3385266)\n\n`pandas` data structures hide a lot of information, and explicitly\nvalidating them at runtime in production-critical or reproducible research\nsettings is a good idea. `pandera` enables users to:\n\n1. Check the types and properties of columns in a `DataFrame` or values in\n a `Series`.\n1. Perform more complex statistical validation like hypothesis testing.\n1. Seamlessly integrate with existing data analysis/processing pipelines\n via function decorators.\n\n`pandera` provides a flexible and expressive API for performing data validation\non tidy (long-form) and wide data to make data processing pipelines more\nreadable and robust.\n\n\n## Documentation\n\nThe official documentation is hosted on ReadTheDocs: https://pandera.readthedocs.io\n\n## Install\n\nUsing pip:\n\n```\npip install pandera\n```\n\nUsing conda:\n\n```\nconda install -c cosmicbboy pandera\n```\n\n## Example Usage\n\n### `DataFrameSchema`\n\n```python\nimport pandas as pd\nimport pandera as pa\n\nfrom pandera import Column, DataFrameSchema, Check\n\n\n# validate columns\nschema = DataFrameSchema({\n # the check function expects a series argument and should output a boolean\n # or a boolean Series.\n \"column1\": Column(pa.Int, Check(lambda s: s <= 10)),\n \"column2\": Column(pa.Float, Check(lambda s: s < -1.2)),\n # you can provide a list of validators\n \"column3\": Column(pa.String, [\n Check(lambda s: s.str.startswith(\"value_\")),\n Check(lambda s: s.str.split(\"_\", expand=True).shape[1] == 2)\n ]),\n})\n\n# alternatively, you can pass strings representing the legal pandas datatypes:\n# http://pandas.pydata.org/pandas-docs/stable/basics.html#dtypes\nschema = DataFrameSchema({\n \"column1\": Column(\"int64\", Check(lambda s: s <= 10)),\n ...\n})\n\ndf = pd.DataFrame({\n \"column1\": [1, 4, 0, 10, 9],\n \"column2\": [-1.3, -1.4, -2.9, -10.1, -20.4],\n \"column3\": [\"value_1\", \"value_2\", \"value_3\", \"value_2\", \"value_1\"]\n})\n\nvalidated_df = schema.validate(df)\nprint(validated_df)\n\n# column1 column2 column3\n# 0 1 -1.3 value_1\n# 1 4 -1.4 value_2\n# 2 0 -2.9 value_3\n# 3 10 -10.1 value_2\n# 4 9 -20.4 value_1\n```\n\n## Development Installation\n\n```\ngit clone https://github.com/pandera-dev/pandera.git\ncd pandera\npip install -r requirements.txt\npip install -e .\n```\n\n## Tests\n\n```\npip install pytest\npytest tests\n```\n\n## Contributing to pandera [](https://github.com/pandera-dev/pandera/graphs/contributors)\n\nAll contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome.\n\nA detailed overview on how to contribute can be found in the [contributing guide](https://github.com/pandera-dev/pandera/blob/master/.github/CONTRIBUTING.md) on GitHub.\n\n## Issues\n\nGo [here](https://github.com/pandera-dev/pandera-dev/issues) to submit feature\nrequests or bugfixes.\n\n## Other Data Validation Libraries\n\nHere are a few other alternatives for validating Python data structures.\n\n**Generic Python object data validation**\n\n- [voloptuous](https://github.com/alecthomas/voluptuous)\n- [schema](https://github.com/keleshev/schema)\n\n**`pandas`-specific data validation**\n\n- [opulent-pandas](https://github.com/danielvdende/opulent-pandas)\n- [PandasSchema](https://github.com/TMiguelT/PandasSchema)\n- [pandas-validator](https://github.com/c-data/pandas-validator)\n- [table_enforcer](https://github.com/xguse/table_enforcer)\n\n## Why `pandera`?\n\n- `pandas`-centric data types, column nullability, and uniqueness are\n first-class concepts.\n- `check_input` and `check_output` decorators enable seamless integration with\n existing code.\n- `Check`s provide flexibility and performance by providing access to `pandas`\n API by design.\n- `Hypothesis` class provides a tidy-first interface for statistical hypothesis\n testing.\n- `Check`s and `Hypothesis` objects support both tidy and wide data validation.\n- Comprehensive documentation on key functionality.\n\n\n### Citation Information\n\n```\n@misc{niels_bantilan_2019_3385266,\n author = {Niels Bantilan and\n Nigel Markey and\n Riccardo Albertazzi and\n chr1st1ank},\n title = {pandera-dev/pandera: 0.2.0 pre-release 1},\n month = sep,\n year = 2019,\n doi = {10.5281/zenodo.3385266},\n url = {https://doi.org/10.5281/zenodo.3385266}\n}\n```\n\n\n",
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