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    "info": {
        "author": "thibaut boissin",
        "author_email": "thibaut.boissin@gmail.com",
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        "description": "# Pandas transform checker\n\n## what is it ?\n\nThis library is focused on data quality checking on pandas transformations.\nTransformations are functions that takes a pandas DataFrame as input ( plus\nother params ) and output a DataFrame.\n\nThis library allow the user to specify a contract that the function must respect.\nIn this contract the user can specify:\n - the added columns\n - the deleted columns\n - the modified columns\n - if the function add/drop records\n - if the function modify the index ( ex: resampling )\n\nOnce the contract if specified, the function will raise a RuntimeError\nif one of it's specifications is violated.\n\n## how to use it ?\n\nThe package contains the decorator that performs the check it can be \nimported the following way:\n```\nfrom pandas_transform_checker.decorator_contract_checker import input_df_contract\n```\n\n### Args\n\ndf_param: name of the param of the function that is the input df\ncontract_params: dict defining the contract of the function in the following format:\n```\ncontract_dict = {\n    \"col_additions\": {\n        \"col_a\": \"int\",\n        \"col_b\": \"float\"\n    },\n    \"col_deletions\": {\n        \"col_c\",\n        \"col_d\"\n    },\n    \"col_editions\": {\n        \"col_e\",\n        \"col_f\"\n    },\n    \"allow_index_edition\": False,\n    \"allow_drop_record\": True\n}\n```\nwhich means that the function must create \"col_a\", \"col_b\", delete \"col_c\", \"col_d\", must\nnot modify any column data except \"col_e\", \"col_f\", and must not edit the index\n\nhere is the list of keys allowed in this dict:\n- col_additions: dict where keys are column names and values are dtypes (string)\n- col_deletions: set of str representing the deleted columns\n- col_editions: set of str representing the modified columns\n- allow_index_edition: bool indicating if the function modify the index\n- allow_add_drop_record (bool): indicate if the function can drop some records (ex. when dropna is used)\n\n### Usage\nwhen you have a function that takes a df as input:\n```\ndef super_func(df_input):\n    ...\n```\njust add the annotation to automatically check properties\n```\n@input_df_contract(df_param=\"df_input\", contract_dict={\"col_editions\": {\"col_e\",\"col_f\"}})\ndef super_func(df_input):\n    ...\n```",
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        "name": "pandas-transform-checker",
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        "summary": "function annotations to check properties on pandas dataframe transformations",
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