Metadata-Version: 2.1
Name: autonormalize
Version: 0.1.2
Summary: a library for automated table normalization
Home-page: http://featuretools.com
Author: Feature Labs, Inc.
Author-email: support@featurelabs.com
License: BSD 3-clause
Description: # AutoNormalize
        
        [![CircleCI](https://circleci.com/gh/FeatureLabs/autonormalize.svg?style=shield&circle-token=b890443ca669d7e88d62ad2fd712f92951550c4a)](https://circleci.com/gh/FeatureLabs/autonormalize)
        
        AutoNormalize is a Python library for automated datatable normalization, intended for use with [Feature Tools](https://github.com/Featuretools/featuretools). AutoNormalize allows you to build an `EntitySet` from a single denormalized table and generate features for machine learning.
        
        Before AutoNormalize:
        
        ![](screenshots/before.png)   
        
        After AutoNormalize:
        
        ![](screenshots/after.png)
        <br />
        ### Install
        ```shell
        pip install autonormalize
        ```
        ### Uninstall
        ```shell
        pip uninstall autonormalize
        ```
        <br />
        
        ### API Reference
        ```shell
        auto_entityset(df, accuracy=0.98, index=None, name=None, time_index=None)
        ```
        Creates a normalized entityset from a dataframe.
        
        Arguments:
        
        `df` (pd.Dataframe) : the dataframe containing data
        
        `accuracy` (0 < float <= 1.00; default = 0.98) : the accuracy threshold required in order to conclude a dependency (i.e. with accuracy = 0.98, 0.98 of the rows must hold true the dependency LHS --> RHS)
        
        `index` (str, optional) : name of column that is intended index of df
        
        `name` (str, optional) : the name of created EntitySet
        
        `time_index` (str, optional) : name of time column in the dataframe.
        
        Returns:
        
        `entityset` (ft.EntitySet) : created entity set
        
        <br />
        
        ```shell
        find_dependencies(df, accuracy=0.98, index=None)
        ```
        Finds dependencies within dataframe with the DFD search algorithm.
        
        Returns:
        
        `dependencies` (Dependencies) : the dependencies found in the data within the contraints provided
        
        <br />
        
        ```shell
        normalize_dataframe(df, dependencies)
        ```
        Normalizes dataframe based on the dependencies given. Keys for the newly created DataFrames can only be columns that are strings, ints, or categories. Keys are chosen according to the priority: 
        1) shortest lenghts 
        2) has "id" in some form in the name of an attribute 
        3) has attribute furthest to left in the table
        
        Returns:x
        
        `new_dfs` (list[pd.DataFrame]) : list of new dataframes
        
        <br />
        
        ```shell
        make_entityset(df, dependencies, name=None, time_index=None):
        ```
        Creates a normalized EntitySet from dataframe based on the dependencies given. Keys are chosen in the same fashion as for `normalize_dataframe`and a new index will be created if any key has more than a single attribute.
        
        Returns:
        
        `entityset` (ft.EntitySet) : created EntitySet
        
        <br />
        
        ## Feature Labs
        <a href="https://www.featurelabs.com/">
            <img src="http://www.featurelabs.com/wp-content/uploads/2017/12/logo.png" alt="Featuretools" />
        </a>
        
        AutoNormalize is an open source project created by [Feature Labs](https://www.featurelabs.com/). To see the other open source projects we're working on visit Feature Labs [Open Source](https://www.featurelabs.com/open). If building impactful data science pipelines is important to you or your business, please [get in touch](https://www.featurelabs.com/contact/).
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
