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    "info": {
        "author": "Onno Valkering",
        "author_email": "onnovalkering@users.noreply.github.com",
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            "License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
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        "description": "\n# Corruptor\n[![PyPI](https://img.shields.io/pypi/v/corruptor.svg)](https://pypi.org/project/corruptor)\n[![PyPI - License](https://img.shields.io/pypi/l/corruptor.svg)](https://pypi.org/project/corruptor)\n[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/corruptor.svg)](https://pypi.org/project/corruptor)\n\nWant to realistically corrupt your (textual) data? Use Corruptor!\n\n```shell\npip install corruptor\n```\n\nThe supported type of corruptions:\n\n- OCR variation\n- Phonetic variation\n- Typing error\n- Edit (insert, delete, replace, swap)\n\n## Getting started\nThere are three different classes that can be used.\n\n### `BasicCorruptor`\nThe basic corruptor provides methods for each type of corruption, using default configuration.\n\n```python\n>>> from corruptor import BasicCorruptor\n>>> basic = BasicCorruptor()\n>>> basic.ocr('johnson')\n'johnst0n'\n>>> basic.phonetic('johnson')\n'johnzon'\n>>> basic.typo('johnson')\n'johhson'\n>>> basic.insert('johnson')\n'johnsson'\n>>> basic.delete('johnson')\n'jhnson'\n>>> basic.replace('johnson')\n'johnsin'\n>>> basic.swap('johnson')\n'johnsno'\n```\n\n### `ProbabilisticCorruptor`\nThis class selects the type of corruption at random, based on provided weights.\n\n```python\n>>> from corruptor import ProbabilisticCorruptor\n>>> prob = ProbabilisticCorruptor({'none': 0.33, 'phonetic': 0.33, 'typo': 0.33})\n>>> prob.corrupt('conner')\n'conner'\n>>> prob.corrupt('conner')\n'conneah'\n>>> prob.corrupt('conner')\n'conber'\n```\n\n### `DataFrameCorruptor`\nIn short, the DataFrame corruptor randomly corrupts `n` rows of a pandas DataFrame.\n\n```python\n>>> import pandas as pd\n>>> from corruptor import DataFrameCorruptor\n>>> df = pd.DataFrame({'firstname': ['frank', 'john'], 'lastname': ['johnson', 'conner']})\n>>> dfc = DataFrameCorruptor({'firstname': (0.5, prob), 'lastname': (0.5, prob)})\n>>> dfc.corrupt(df, n=2)\n  firstname lastname\n0     frahk  johnson\n1      john   conber\n```\n\n\n\n\n\n\n",
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