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        "description": ".. image:: https://circleci.com/gh/keitakurita/torchtable.svg?style=svg\n    :target: https://circleci.com/gh/keitakurita/torchtable\n\n.. image:: https://readthedocs.org/projects/torchtable/badge/?version=master\n    :target: https://torchtable.readthedocs.io/en/master/?badge=master\n    :alt: Documentation Status\n\ntorchtable\n++++++++++\n\nTorchtable is a library for handling tabular datasets in PyTorch. It is heavily inspired by torchtext and uses a similar API but without some of the limitations (e.g. only one field per column).\nTorchtable aims to be **simple to use** and **easily extensible**. \nIt provides sensible defaults while allowing the user to define their own custom pipelines, putting all of this behind an intuitive interface.\n\nInstallation\n============\nInstall via pip.\n\n`$ pip install torchtable`\n\nDocumentation\n=============\nDocumentation is a work in progress, but the current docs can be read `here <https://torchtable.readthedocs.io/en/master/>`_.\nIn addition, you can read the notebooks in the examples directory or dev_nb directory to learn more.\n\nUsage\n=====\n\nTorchtable uses a declarative API similar to torchtext.\nHere is an example of how you might handle an imaginary dataset where you are supposed to predict the price of some product.\n\n.. code-block:: python\n\n  >>> train = TabularDataset.from_csv('data/train.csv',\n  ...    fields={'seller_id': CategoricalField(min_freq=3),\n  ...            'timestamp': [DayofWeekField(), HourField()],\n  ...            'price': NumericalField(fill_missing=\"median\", is_target=True)\n  ...    })\n  ...\n\nSee the examples directory for more examples.\n\nTODO\n====\n- Add more models\n- Implement default field selection\n- Implement text field/operations\n- Implement swap noise\n- Implement input/output validation\n\n\n",
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