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README.md

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[![CircleCI](https://circleci.com/gh/FeatureLabs/autonormalize.svg?style=shield&circle-token=b890443ca669d7e88d62ad2fd712f92951550c4a)](https://circleci.com/gh/FeatureLabs/autonormalize)
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AutoNormalize is a Python library for automated datatable normalization, intended for use with [Featuretools](https://github.com/Featuretools/featuretools). AutoNormalize allows you to build an `EntitySet` from a single denormalized table and generate features for machine learning.
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AutoNormalize is a Python library for automated datatable normalization. It allows you to build an `EntitySet` from a single denormalized table and generate features for machine learning using [Featuretools](https://github.com/Featuretools/featuretools).
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![](gif.gif)
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<img src=https://github.com/FeatureLabs/autonormalize/blob/master/gif.gif>
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### Install
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## Getting Started
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* [Install](#install)
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* [Demos](#demos)
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* [API Reference](#api-reference)
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## Install
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```shell
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pip install featuretools[autonormalize]
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```
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### Uninstall
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#### Uninstall
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```shell
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pip uninstall autonormalize
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```
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<br />
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### API Reference
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## Demos
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#### `auto_entityset`
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* [Blog Post](https://blog.featurelabs.com/automatic-dataset-normalization-for-feature-engineering-in-python/)
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* [Machine Learning Demo with Featuretools](https://github.com/FeatureLabs/autonormalize/blob/master/autonormalize/demos/AutoNormalize%20%2B%20FeatureTools%20Demo.ipynb)
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* [Kaggle Liquor Sales Dataset Demo](https://github.com/FeatureLabs/autonormalize/blob/master/autonormalize/demos/Kaggle%20Liquor%20Sales%20Dataset%20Demo.ipynb)
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* [Demo with Editing Dependencies](https://github.com/FeatureLabs/autonormalize/blob/master/autonormalize/demos/Editing%20Dependnecies%20Demo.ipynb)
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* [Kaggle Food Production Dataset Demo](https://github.com/FeatureLabs/autonormalize/blob/master/autonormalize/demos/Kaggle%20Food%20%20Dataset%20Demo.ipynb)
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## API Reference
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### `auto_entityset`
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```shell
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auto_entityset(df, accuracy=0.98, index=None, name=None, time_index=None)
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```
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* `entityset` (ft.EntitySet) : created entity set
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<br />
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#### `find_dependencies`
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### `find_dependencies`
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```shell
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find_dependencies(df, accuracy=0.98, index=None)
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* `dependencies` (Dependencies) : the dependencies found in the data within the contraints provided
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<br />
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#### `normalize_dataframe`
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### `normalize_dataframe`
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```shell
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normalize_dataframe(df, dependencies)
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<br />
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#### `make_entityset`
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### `make_entityset`
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```shell
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make_entityset(df, dependencies, name=None, time_index=None)
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<br />
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#### `normalize_entity`
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### `normalize_entity`
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```shell
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normalize_entity(es, accuracy=0.98)
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<br />
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### Demos
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* [Machine Learning Demo with Featuretools](https://github.com/FeatureLabs/autonormalize/blob/master/autonormalize/demos/AutoNormalize%20%2B%20FeatureTools%20Demo.ipynb)
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* [Kaggle Liquor Sales Dataset Demo](https://github.com/FeatureLabs/autonormalize/blob/master/autonormalize/demos/Kaggle%20Liquor%20Sales%20Dataset%20Demo.ipynb)
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* [Demo with Editing Dependencies](https://github.com/Featuretools/featuretools/pull/699)
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* [Kaggle Food Production Dataset Demo](https://github.com/FeatureLabs/autonormalize/blob/master/autonormalize/demos/Kaggle%20Food%20%20Dataset%20Demo.ipynb)
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<br />
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## Feature Labs
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<a href="https://www.featurelabs.com/">
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<img src="http://www.featurelabs.com/wp-content/uploads/2017/12/logo.png" alt="Featuretools" />

setup.py

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setup(
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name='autonormalize',
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version='1.0.0',
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version='1.0.1',
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description='a library for automated table normalization',
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url='https://github.com/FeatureLabs/autonormalize',
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license='BSD 3-clause',

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