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        "author": "Masashi Shibata <contact@c-bata.link>",
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        "description": "================\npandas-validator\n================\n\n.. image:: https://travis-ci.org/c-bata/pandas-validator.svg?branch=master\n    :target: https://travis-ci.org/c-bata/pandas-validator\n\n.. image:: https://badge.fury.io/py/pandas_validator.svg\n    :target: http://badge.fury.io/py/pandas_validator\n\n.. image:: https://readthedocs.org/projects/pandas-validator/badge/?version=latest\n    :target: https://readthedocs.org/projects/pandas-validator/?badge=latest\n    :alt: Documentation Status\n\n\nValidates the pandas object such as DataFrame and Series.\nAnd this can define validator like django form class.\n\n\nWhy bugs occur in Data Wrangling with pandas\n--------------------------------------------\n\nWhen we wrangle our data with pandas, We use `DataFrame` frequently.\n`DataFrame` is very powerfull and easy to handle.\nBut `DataFrame` has no it's schema, so It allows irregular values without being aware of it.\nWe are confused by these values and affect the results of data wrangling.\n\n`pandas-schema` offers the functions for validating `DataFrame` or `Series` objects and generating factory data.\n\n\nOverview\n--------\n\n.. code-block:: python\n\n    import pandas as pd\n    import pandas_validator as pv\n\n    class SampleDataFrameValidator(pv.DataFrameValidator):\n        row_num = 5\n        column_num = 2\n        label1 = pv.IntegerColumnValidator('label1', min_value=0, max_value=10)\n        label2 = pv.FloatColumnValidator('label2', min_value=0, max_value=10)\n\n    validator = SampleDataFrameValidator()\n\n    df = pd.DataFrame({'label1': [0, 1, 2, 3, 4], 'label2': [5.0, 6.0, 7.0, 8.0, 9.0]})\n    validator.is_valid(df)  # True.\n\n    df = pd.DataFrame({'label1': [11, 12, 13, 14, 15], 'label2': [5.0, 6.0, 7.0, 8.0, 9.0]})\n    validator.is_valid(df)  # False.\n\n    df = pd.DataFrame({'label1': [0, 1, 2], 'label2': [5.0, 6.0, 7.0]})\n    validator.is_valid(df)  # False\n\n\nGetting Started\n===============\n\nRequirements\n------------\n\n* Support python version: 2.7, 3.4, 3.5, 3.6\n* Support pandas version: 0.18, 0.19\n\nInstallation\n------------\n\n.. code-block:: console\n\n    $ pip install pandas_validator\n\nUsage\n-----\n\nPlease see the following demo written by ipython notebook.\n\n* `Demo in Japanese <https://github.com/c-bata/pandas-validator/blob/master/example/pandas_validator_example_ja.ipynb>`_\n* `Demo in English <https://github.com/c-bata/pandas-validator/blob/master/example/pandas_validator_example_en.ipynb>`_\n\n\nLicense\n=======\n\nThis software is licensed under the MIT License.\n\n\nResources\n=========\n\n* `Github <https://github.com/c-bata/pandas-validator>`_\n* `PyPI <https://pypi.python.org/pypi/pandas_validator>`_\n\n\nCHANGES\n=======\n\n0.5.0 (2017-01-06)\n------------------\n\n* Add LambdaColumnValidator\n* Add IndexValidator\n* ``.validate(df)`` method is deprecated. Please use ``.is_valid(df, raise_exception=True)``\n\n0.4.0 (2015-10-28)\n------------------\n\n* Hot fix: cannot include source file\n\n0.3.2 (2015-10-28)\n------------------\n\n* Python 2.7, 3.2, 3.3, 3.4, 3.5 support\n* pandas 0.14, 0.15, 0.16, 0.17 support\n\n0.3.1 (2015-10-28)\n------------------\n\n* Update support python version\n* Update dependencies library version\n\n0.3.0 (2015-07-15)\n------------------\n\n* Critical bug fix\n\n0.2.0 (2015-05-24)\n------------------\n\n* Support char type validation\n* flake8 testing\n\n0.1.0 (2015-05-22)\n------------------\n\nInitial release.\n\n* Support integer series validator\n* Support float series validator\n* Support dataframe validator\n\n* Testing on python2.7 and python 3.4\n\n0.0.0 (2015-05-17)\n------------------\n\nCreate this project.\n\n\n\n",
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