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        "author": "Cedric Canovas",
        "author_email": "dev@canovas.me",
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        "description": "# Overview\n\n`dataenforce` is a Python package used to enforce column names & types of pandas DataFrames using Python 3 type hinting.\n\nIt is a common issue in Data Analysis to pass dataframes into functions without a clear idea of which columns are included or not, and as columns are added to or removed from input data, code can break in unexpected ways. With `dataenforce`, you can provide a clear interface to your functions and ensure that the input dataframes will have the right format when your code is used.\n\n# How to install\n\nInstall with pip:\n```\npip install dataenforce\n```\n\nYou can also pip install it from the sources, or just import the `dataenforce` folder.\n\n# How to use\n\nThere are two parts in `dataenforce`: the type-hinting part, and the validation. You can use type-hinting with the provided class to indicate what shape the input dataframes should have, and the validation decorator to additionally ensure the format is respected in every function call.\n\n## Type-hinting: `Dataset`\n\nThe `Dataset` type indicates that we expect a `pandas.DataFrame`\n\n### Column name checking\n\n```py\nfrom dataenforce import Dataset\n\ndef process_data(data: Dataset[\"id\", \"name\", \"location\"])\n  pass\n```\n\nThe code above specifies that `data` must be a DataFrame with exactly the 3 mentioned columns. If you want to only specify a subset of columns which is required, you can use an ellipsis:\n```py\ndef process_data(data: Dataset[\"id\", \"name\", \"location\", ...])\n  pass\n```\n\n### dtype checking\n\n```py\ndef process_data(data: Dataset[\"id\": int, \"name\": object, \"latitude\": float, \"longitude\": float])\n  pass\n```\n\nThe code above specifies the column names which must be there, with associated types. A combination of only names & with types is possible: `Dataset[\"id\": int, \"name\"]`.\n\n### Reusing dataframe formats\n\nAs you're likely to use the same column subsets several times in your code, you can define them to reuse & combine them later:\n```py\nDName = Dataset[\"id\", \"name\"]\nDLocation = Dataset[\"id\", \"latitude\", \"longitude\"]\n\n# Expects columns id, name\ndef process1(data: DName):\n  pass\n\n# Expects columns id, name, latitude, longitude, timestamp\ndef process2(data: Dataset[DName, DLocation, \"timestamp\"])\n  pass\n```\n\n## Enforcing: `@validate`\n\nThe `@validate` decorator ensures that input `Dataset`s have the right format when the function is called, otherwise raises `TypeError`.\n\n```py\nfrom dataenforce import Dataset, validate\nimport pandas as pd\n\n@validate\ndef process_data(data: Dataset[\"id\", \"name\"]):\n  pass\n\nprocess_data(pd.DataFrame(dict(id=[1,2], name=[\"Alice\", \"Bob\"]))) # Works\nprocess_data(pd.DataFrame(dict(id=[1,2]))) # Raises a TypeError, column name missing\n```\n\n# How to test\n\n`dataenforce` uses `pytest` as a testing library. If you have `pytest` installed, just run `pytest` in the command line while being in the root folder.\n\n# Notes\n\n* You can use `dataenforce` to type-hint the return value of a function, but it is not currently possible to `validate` it (it is not included in the checks)\n* `dataenforce` is released under the Apache License 2.0, meaning you can freely use the library and redistribute it, provided Copyright is kept\n* Dependencies: Pandas & Numpy\n\n\n",
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