[
  {
    "path": ".gitignore",
    "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n"
  },
  {
    "path": "LICENSE",
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  },
  {
    "path": "README.md",
    "content": "# 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 `PYTHONPATH=\".\" 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* You can't use `@validate` on a function where you use non-base class type-hints as strings (like `def f() -> \"MyClass\"`). Issue related to PEP 563\n* This work is at experimental state. It is not production-ready. Please raise issues & send pull requests if you find/solve some bugs\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* Tested with Python 3.6, 3.7, 3.8"
  },
  {
    "path": "dataenforce/__init__.py",
    "content": "# Copyright 2018 Cedric Canovas\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nfrom functools import wraps\nimport pandas as pd\nfrom typing import _TypingEmpty, _tp_cache, Generic, get_type_hints\nimport numpy as np\ntry:\n    from typing import GenericMeta # Python 3.6\nexcept ImportError: # Python 3.7 \n    class GenericMeta(type): pass\n\ndef validate(f):\n    signature = inspect.signature(f)\n    hints = get_type_hints(f)\n\n    @wraps(f)\n    def wrapper(*args, **kwargs):\n        bound = signature.bind(*args, **kwargs)\n\n        for argument_name, value in bound.arguments.items():\n            if argument_name in hints and isinstance(hints[argument_name], DatasetMeta):\n                hint = hints[argument_name]\n\n                if not isinstance(value, pd.DataFrame):\n                    raise TypeError(\"%s is not a pandas Dataframe\" % value)\n                columns = set(value.columns)\n                if hint.only_specified and not columns == hint.columns:\n                    raise TypeError(\"%s columns do not match required column set %s\" % (argument_name, hint.columns))\n                if not hint.only_specified and not hint.columns.issubset(columns):\n                    raise TypeError(\"%s is missing some columns from %s\" % (argument_name, hint.columns))\n                if hint.dtypes:\n                    dtypes = dict(value.dtypes)\n                    for colname, dt in hint.dtypes.items():\n                        if not np.issubdtype(dtypes[colname], np.dtype(dt)):\n                            raise TypeError(\"%s is not a subtype of %s for column %s\" % (dtypes[colname], dt, colname))\n        return f(*args, **kwargs)\n\n    return wrapper\n\ndef _get_columns_dtypes(p):\n    columns = set()\n    dtypes = {}\n\n    if isinstance(p, str):\n        columns.add(p)\n    elif isinstance(p, slice):\n        columns.add(p.start)\n        if not inspect.isclass(p.stop):\n            raise TypeError(\"Column type hints must be classes, error with %s\" % repr(p.stop))\n        dtypes[p.start] = p.stop\n    elif isinstance(p, (list, set)):\n        for el in p:\n            subcolumns, subdtypes = _get_columns_dtypes(el)\n            columns |= subcolumns\n            dtypes.update(subdtypes)\n    elif isinstance(p, DatasetMeta):\n        columns |= p.columns\n        dtypes.update(p.dtypes)\n    else:\n        raise TypeError(\"Dataset[col1, col2, ...]: each col must be a string, list or set.\")\n\n    return columns, dtypes\n\nclass DatasetMeta(GenericMeta):\n    \"\"\"Metaclass for Dataset (internal).\"\"\"\n\n    def __new__(metacls, name, bases, namespace, **kargs):\n        return super().__new__(metacls, name, bases, namespace)\n\n    @_tp_cache\n    def __getitem__(self, parameters):\n        if hasattr(self, '__origin__') and (self.__origin__ is not None or self._gorg is not Dataset):\n            return super().__getitem__(parameters)\n        if parameters == ():\n            return super().__getitem__((_TypingEmpty,))\n        if not isinstance(parameters, tuple):\n            parameters = (parameters,)\n        parameters = list(parameters)\n\n        only_specified = True\n        if parameters[-1] is ...:\n            only_specified = False\n            parameters.pop()\n\n        columns, dtypes = _get_columns_dtypes(parameters)\n\n        meta = DatasetMeta(self.__name__, self.__bases__, {})\n        meta.only_specified = only_specified\n        meta.columns = columns\n        meta.dtypes = dtypes\n\n        return meta\n\nclass Dataset(pd.DataFrame, extra=Generic, metaclass=DatasetMeta):\n    \"\"\"Defines type Dataset to serve as column name & type enforcement for pandas DataFrames\"\"\"\n    __slots__ = ()\n\n    def __new__(cls, *args, **kwds):\n        if not hasattr(cls, '_gorg') or cls._gorg is Dataset:\n            raise TypeError(\"Type Dataset cannot be instantiated; \"\n                            \"use pandas.DataFrame() instead\")\n        return _generic_new(pd.DataFrame, cls, *args, **kwds)\n"
  },
  {
    "path": "setup.py",
    "content": "import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read().replace(\"```py\", \"```\")\n\nsetuptools.setup(\n    name=\"dataenforce\",\n    version=\"0.1.2\",\n    author=\"Cedric Canovas\",\n    author_email=\"dev@canovas.me\",\n    description=\"Enforce column names & data types of pandas DataFrames\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/CedricFR/dataenforce\",\n    packages=setuptools.find_packages(),\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"Operating System :: OS Independent\",\n        \"License :: OSI Approved :: Apache Software License\"\n    ],\n)\n"
  },
  {
    "path": "tests/test_dataset.py",
    "content": "import pytest\nfrom dataenforce import Dataset\n\ndef test_empty():\n    DEmpty = Dataset[...]\n\n    assert DEmpty.columns == set()\n    assert DEmpty.dtypes == {}\n    assert DEmpty.only_specified == False\n\ndef test_columns():\n    DName = Dataset[\"id\", \"name\"]\n\n    assert DName.columns == {\"id\", \"name\"}\n    assert DName.dtypes == {}\n    assert DName.only_specified == True\n\ndef test_ellipsis():\n    DName = Dataset[\"id\", \"name\", ...]\n\n    assert DName.columns == {\"id\", \"name\"}\n    assert DName.dtypes == {}\n    assert DName.only_specified == False\n\ndef test_dtypes():\n    DName = Dataset[\"id\": int, \"name\": object, \"location\"]\n\n    assert DName.columns == {\"id\", \"name\", \"location\"}\n    assert DName.dtypes == {'id': int, 'name': object}\n    assert DName.only_specified == True\n\ndef test_nested():\n    DName = Dataset[\"id\": int, \"name\": object]\n    DLocation = Dataset[\"id\": int, \"longitude\": float, \"latitude\": float]\n\n    DNameLoc = Dataset[DName, DLocation]\n\n    assert DNameLoc.columns == {\"id\", \"name\", \"longitude\", \"latitude\"}\n    assert DNameLoc.dtypes == {'id': int, 'name': object, \"longitude\": float, \"latitude\": float}\n    assert DNameLoc.only_specified == True\n\n    DNameLocEtc = Dataset[DNameLoc, \"description\": object, ...]\n    assert DNameLocEtc.columns == {\"id\", \"name\", \"longitude\", \"latitude\", \"description\"}\n    assert DNameLocEtc.dtypes == {'id': int, 'name': object, \"longitude\": float, \"latitude\": float, \"description\": object}\n    assert DNameLocEtc.only_specified == False\n\ndef test_init():\n    with pytest.raises(TypeError):\n        Dataset()\n"
  },
  {
    "path": "tests/test_validate.py",
    "content": "import pytest\nfrom dataenforce import Dataset, validate\nimport pandas as pd\nimport numpy as np\nfrom datetime import datetime\n\ndef test_validate_columns():\n    df1 = pd.DataFrame(dict(a=[1,2,3]))\n    df2 = pd.DataFrame(dict(a=[1,2,3], b=[4,5,6]))\n    df3 = pd.DataFrame(dict(a=[1,2,3], b=[4,5,6], c=[7,8,9]))\n\n    @validate\n    def process(data: Dataset[\"a\", \"b\"]):\n        pass\n\n    process(df2)\n    with pytest.raises(TypeError):\n        process(df1)\n    with pytest.raises(TypeError):\n        process(df3)\n\ndef test_validate_combination():\n    df1 = pd.DataFrame(dict(a=[1,2,3]))\n    df2 = pd.DataFrame(dict(a=[1,2,3], b=[4,5,6]))\n\n    @validate\n    def process(data1: Dataset[\"a\"], data2: Dataset[\"a\", \"b\"]):\n        pass\n\n    process(df1, df2)\n\ndef test_validate_ellipsis():\n    df1 = pd.DataFrame(dict(a=[1,2,3]))\n    df2 = pd.DataFrame(dict(a=[1,2,3], b=[4,5,6]))\n    df3 = pd.DataFrame(dict(a=[1,2,3], b=[4,5,6], c=[7,8,9]))\n\n    @validate\n    def process(data: Dataset[\"a\", \"b\", ...]):\n        pass\n\n    process(df2)\n    process(df3)\n    with pytest.raises(TypeError):\n        process(df1)\n\ndef test_validate_empty():\n    df = pd.DataFrame(dict(a=[1,2,3]))\n\n    @validate\n    def process(data: Dataset[...]):\n        pass\n\n    process(df)\n\n    with pytest.raises(TypeError):\n        process(False)\n\ndef test_validate_dtypes():\n    df = pd.DataFrame(dict(a=[1,2,3], b=[4.1,5.1,6.1], c=[\"a\", \"b\", \"c\"], d=[datetime.now().replace(hour=x) for x in [7,8,9]]))\n\n    @validate\n    def process1(data: Dataset[\"a\": int, \"b\": np.float, \"c\": object, \"d\": np.datetime64]):\n        pass\n    @validate\n    def process2(data: Dataset[\"a\": float, \"b\", ...]):\n        pass\n    @validate\n    def process3(data: Dataset[\"a\": np.datetime64, ...]):\n        pass\n\n    process1(df)\n    with pytest.raises(TypeError):\n        process2(df)\n    with pytest.raises(TypeError):\n        process3(df)\n\ndef test_validate_other_types():\n    df = pd.DataFrame(dict(a=[1,2,3]))\n\n    @validate\n    def process(data: Dataset[\"a\"], other: int):\n        pass\n\n    process(df, 3)\n\ndef test_return_type():\n    df = pd.DataFrame(dict(a=[1,2,3]))\n\n    class Klass:\n        pass\n\n    @validate\n    def process(data: Dataset[\"a\"]) -> int:\n        return 2\n    \n    @validate\n    def process2(data: Dataset[\"a\"]) -> Klass:\n        return Klass()\n\n    #@validate\n    #def process3(data: Dataset[\"a\"]) -> \"Klass\":\n    #    return Klass()\n\n    process(df)\n    process2(df)\n    #process3(df) # -> This scenario fails, issue with eval in get_type_hints (read PEP 563)"
  }
]