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        "description": "# marshmallow-dataframe\n\n[![Build Status](https://travis-ci.org/facultyai/marshmallow-dataframe.svg?branch=master)](https://travis-ci.org/facultyai/marshmallow-dataframe)\n[![PyPI](https://img.shields.io/pypi/v/marshmallow-dataframe.svg)](https://pypi.org/project/marshmallow-dataframe/)\n[![License](https://img.shields.io/github/license/facultyai/marshmallow-dataframe.svg)](https://github.com/facultyai/marshmallow-dataframe/blob/master/LICENSE)\n\n`marshmallow-dataframe` is a library that helps you generate\n[marshmallow](https://marshmallow.readthedocs.io/) Schemas for Pandas\nDataFrames.\n\n# Usage\n\nLet's start by creating an example dataframe for which we want to create a\n`Schema`. This dataframe has four columns: two of them are of string type, one\nis a float, and the last one is an integer.\n\n```python\nimport pandas as pd\nimport numpy as np\nfrom marshmallow_dataframe import SplitDataFrameSchema\n\nanimal_df = pd.DataFrame(\n    [\n        (\"falcon\", \"bird\", 389.0, 2),\n        (\"parrot\", \"bird\", 24.0, 2),\n        (\"lion\", \"mammal\", 80.5, 4),\n        (\"monkey\", \"mammal\", np.nan, 4),\n    ],\n    columns=[\"name\", \"class\", \"max_speed\", \"num_legs\"],\n)\n```\n\nYou can then create a marshmallow schema that will validate and load dataframes\nthat follow the same structure as the one above and that have been serialized\nwith `DataFrame.to_json` with the [`orient=split`\nformat](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_json.html#pandas.DataFrame.to_json).\nThe `dtypes` attribute of the `Meta` class is required, and other [`marshmallow`\nSchema\noptions](https://marshmallow.readthedocs.io/en/latest/api_reference.html#marshmallow.Schema.Meta)\ncan also be passed as attributes of `Meta`:\n\n```python\nclass AnimalSchema(SplitDataFrameSchema):\n    \"\"\"Automatically generated schema for animal dataframe\"\"\"\n\n    class Meta:\n        dtypes = animal_df.dtypes\n```\n\nWhen passing a valid payload for a new animal, this schema will validate it and\nbuild a dataframe:\n\n```python\nanimal_schema = AnimalSchema()\n\nnew_animal = {\n    \"data\": [(\"leopard\", \"mammal\", 58.0, 4), (\"ant\", \"insect\", 0.288, 6)],\n    \"columns\": [\"name\", \"class\", \"max_speed\", \"num_legs\"],\n    \"index\": [0, 1],\n}\n\nnew_animal_df = animal_schema.load(new_animal)\n\nprint(type(new_animal_df))\n# <class 'pandas.core.frame.DataFrame'>\nprint(new_animal_df)\n#       name   class  max_speed  num_legs\n# 0  leopard  mammal     58.000         4\n# 1      ant  insect      0.288         6\n```\n\nHowever, if we pass a payload that doesn't conform to the schema, it will raise\na marshmallow `ValidationError` exception with informative message about errors:\n\n```python\ninvalid_animal = {\n    \"data\": [(\"leopard\", \"mammal\", 58.0, \"four\")],  # num_legs is not an int\n    \"columns\": [\"name\", \"class\", \"num_legs\"],  # missing  max_speed column\n    \"index\": [0],\n}\n\nanimal_schema.load(invalid_animal)\n\n# Raises:\n# marshmallow.exceptions.ValidationError: {\n#     'columns': [\"Must be equal to ['name', 'class', 'max_speed', 'num_legs'].\"],\n#     'data': {0: {3: ['Not a valid integer.']}}\n# }\n```\n\n`marshmallow_dataframe` can also generate Schemas for the `orient=records`\nformat by following the above steps but using\n`marshmallow_dataframe.RecordsDataFrameSchema` as the superclass for\n`AnimalSchema`.\n\n# Installation\n\nmarshmallow-dataframe requires Python >= 3.6 and marshmallow >= 3.0. You can\ninstall it with pip:\n\n```\npip install marshmallow-dataframe\n```\n\n# Contributing\n\nContributions are welcome!\n\nYou can report a problem or feature request in the [issue\ntracker](https://github.com/facultyai/marshmallow-dataframe/issues). If you feel\nthat you can fix it or implement it, please submit a pull request referencing\nthe issues it solves.\n\nUnit tests written using the [`pytest`](https://pytest.org) framework are in the\n`tests` directory, and are run using\n[tox](https://tox.readthedocs.io/en/latest/) on Python 3.6 and 3.7. You can run\nthe tests by installing tox:\n```\npip install tox\n```\nand running the linters and tests for all Python versions by running `tox`, or\nfor a specific Python version by running:\n```\ntox -e py36\n```\n\nWe format the code with [black](https://github.com/python/black), and you can\nformat your checkout of the code before commiting it by running:\n```\ntox -e black -- .\n```\n\n\n",
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