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        "description": "# Pandas ML Utils\n\nPandas Machine Learning Utilities is part of a bigger set of libraries for a convenient experience. Usually exploring\nstatistical models start with a pandas `DataFrame`. \n\nBut soon enough you will find yourself converting your data frames to numpy, splitting arrays, applying min\nmax scalers, lagging and concatenating columns etc. As a result your notebook looks messy and became and \nunreadable beast. Yet the mess becomes only worse once you start to deploy your research into a productive\napplication. The untested hard coded data pipelines need be be maintained at two places. \n\nThe aim of this library is to conveniently operate with data frames without and abstract away the ugly unreproducible \ndata pipelines. The only thing you need is the original unprocessed data frame where you started.\nThe data pipeline becomes a part of your model and gets saved that way. Going into production is as easy as \nthis:\n\n```python\n\nimport pandas as pd\nimport pandas_ml_utils  # monkey patch the `DataFrame`\nfrom pandas_ml_utils import Model\n# alternatively as a one liner `from pandas_ml_utils import pd, Model` \n\nmodel = Model.load('your_saved.model')\ndf = pd.read_csv('your_raw_data.csv')\ndf_prediction = df.model.predict(model)\n\n# do something with your prediction\ndf_prediction.plot()\n``` \n\n\nis intended to help you through your journey of statistical or machine learning models, \nwhile you never need to leave the world of pandas.\n\n## Installation\nThe basic implementation supports [scikit learn][e1] classifiers and regressors.\n```shell script\n\npip install pandas-ml-utils\n```\n\nAdditional machine learning libraries are available as an add on:\n```shell script\n\npip install pandas-ml-utils-torch  # pytorch implementation\npip install pandas-ml-utils-keras  # keras + tensorflow 1.x implementation\n```\n\nNote that the keras/tensorflow version is currently stalled as I focus on pytorch recently. This might change\nwith PyMC4 and tensorflow probability\n \n## Example\nYou will find some demo projects in the [examples][ghl1] directory. But It might also be worth it to check\nthe unit tests and the [integration tests][ghl2]. Here is how classification challenge\nmight look like:\n  \n![Classification Example][ghi1]\n\n\n[e1]: https://scikit-learn.org/stable/\n[ghl1]: ./examples/\n[ghl2]: ../pandas-ml-1ntegration-test\n[ghi1]: ../.readme/images/classification.png",
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