Metadata-Version: 2.1
Name: wrangles
Version: 0.4
Summary: Wrangle your data into shape with machine learning
Home-page: https://wrangles.io
Author: WrangleWorks
Author-email: chris@wrangleworks.com
License: UNKNOWN
Description: # Wrangles
        
        Full documentation available at [wrangles.io](https://wrangles.io/python).
        
        ## What are Wrangles?
        
        Wrangles are a set of modular transformations for data cleaning and enrichment. Each Wrangle is optimized for a particular job, many of which are backed by sophisticated machine learning models.
        
        With Wrangles, you can:
        - Extract information from a set of messy descriptions.
        - Predict which category items belong to.
        - Standardize text data to a desired format.
        - Move data from one system to another.
        - Much more...
        
        Wrangles are system independent, and allow you to pull data from one system, transform it and push it to another. Wrangles can be incorporated directly into python code, or an automated sequence of wrangles can be run as a recipe.
        
        ## Installation
        
        The python package can be installed using [pip](https://pip.pypa.io/en/stable/getting-started/).
        
        ```shell
        pip install wrangles
        ```
        
        Once installed, import the package into your code.
        ```python
        import wrangles
        ```
        
        ## Authentication
        Some Wrangles use cloud based machine learning models. To use them a WrangleWorks account is required.
        
        > Create a WrangleWorks account: [Register](https://sso.wrangle.works/auth/realms/wrwx/protocol/openid-connect/registrations?client_id=account&response_type=code&scope=openid%20email&redirect_uri=https://sso.wrangle.works/auth/realms/wrwx/account/#/)
        
        There are two ways to provide the credentials:
        
        ### Environment Variables
        The credentials can be saved as the environment variables:
        
        - `WRANGLES_USER`
        - `WRANGLES_PASSWORD`
        
        ### Method
        The credentials can be provided within the python code using the authenticate method, prior to calling other functions.
        ```python
        wrangles.authenticate('<user>', '<password>')
        ```
        
        ## Usage
        
        ### Functions
        
        Wrangles can be used as functions, directly incorporated into python code.
        
        Wrangles broadly accept a single input string, or a list of strings. If a list is provided, the results will be returned in an equivalent list in the same order and length as the original.
        
        ```python
        # Extract alphanumeric codes from a free text strings - e.g. find all part numbers in a set of product description
        >>> import wrangles
        
        >>> wrangles.extract.codes('replacement part ABCD1234ZZ')
        ['ABCD1234ZZ']
        
        >>> wrangles.extract.codes(['replacement part ABCD1234ZZ', 'NNN555BBB this one has two XYZ789'])
        [
            ['ABCD1234ZZ'],
            ['NNN555BBB', 'XYZ789']
        ]
        ```
        
        ### Recipes
        
        Recipes are written in YAML and allow a series of Wrangles to be run as an automated sequence.
        
        ```python
        """
        PYTHON
        """
        import wrangles
        wrangles.recipe.run('recipe.wrgl.yml')
        ```
        
        ```yaml
        # file: recipe.wrgl.yml
        # ---
        # Convert a CSV file to an Excel file
        # and change the case of a column.
        read:
          - file:
              name: file.csv
        
        wrangles:
          - convert.case:
              input: my column
              case: upper
        
        write:
          - file:
              name: file.xlsx
        ```
        
        
Keywords: data,wrangling
Platform: UNKNOWN
Description-Content-Type: text/markdown
