Metadata-Version: 2.1
Name: csv-etl
Version: 0.1.1
Summary: UNKNOWN
Home-page: https://github.com/winslowdibona/csv-etl
Author: Winslow DiBona
License: MIT
Description: # csv-etl
        
        A rules based approach to performing ETL operations on csv files.
        
        ## Installation
        
        Requires Python 3
        
        ### Using pip
        
        ```bash
        python -m venv venv
        source venv/bin/activate
        pip install csv-etl
        ```
        
        ### Locally
        
        ```bash
        git clone https://github.com/winslowdibona/csv-etl.git
        cd csv-etl
        python -m venv venv
        source venv/bin/activate
        pip install --editable .
        ```
        
        ### Developing
        
        ```bash
        git clone https://github.com/winslowdibona/csv-etl.git
        cd csv-etl
        python -m venv venv
        source venv/bin/activate
        pip install -r requirements.txt
        ```
        
        ## Overview
        
        The goal of this project is to provide a re-usable way to perform ETL operations on csv files. This implementation takes a given row of data from a csv file and applies a set of rules to generate a new format of the data.
        
        ### Rule Overview
        
        The `Rule` in this project has the following properties
        
         - `target`
         - `source`
         - `type`
         - `input_type`
         - `output_type`
         - `operations`
        
        Using different combinations of values for these properties can give us a flexible toolset for extracting data.
        
        ##### target - `str`
        
        Once we have the extracted the value from the csv and applied our rule to it, the resulting value will appear under this value as the key in the result
        
        ##### source - `str/list`
        
        This is where we want to pull the data from in the csv. If the source is a string, it will fetch that single column. If the source is a list, it will fetch all of the values.
        
        ##### type - `RuleType`
        
        This can be one of two options
        
         - `Static`
         - `Calculation`
        
        If RuleType.Static, the rule will simply return the value stored in Rule.source under Rule.target
        
        If RuleType.Calculation, the rule will fetch the value(s) defined in Rule.source, and perform the operations on them
        
        ##### input_type - `InputType`
        
        The data type you would like to read the value in from the csv as.
        
        This can be one of three options
        
         - `String`
         - `Integer`
         - `Decimal`
        
        ##### output_type - `OutputType`
        
        The data type you would like the resulting value to be.
        
        This can be one of four options
        
         - `String`
         - `Integer`
         - `Decimal`
         - `Date`
        
        ##### operations - `list`
        
        A list of strings that will be run through python `eval` statement. The value(s) extracted from the csv will be available for use in these operations. If there is only a single source, the value will be assigned to the variable `s`. If there are multiple sources, the values will be passed in as a list under the variable `s`.
        
        ### Defining Rules
        
        These rules can be defined programmatically, or via a YAML configuration. The configuraiton follows the below structure
        
        ```yaml
        rules:
          -
            target: target_name
            type: Static || Calculation
            input_type: String || Integer || Decimal
            output_type: String || Integer || Decimal || Date
            source: source_name || [source, names]
            operations: ['operations', 'to', 'run']
        ```
        
        ### Converting CSV Data
        
        Once we have a set of rules, we can use the `CSVConverter` class to execute our rules on a data set.
        
        ```python
        from csv_etl import CSVConverter
        
        csv_converter = CSVConverter(rules)
        
        result = csv_converter.convert('path/to/csv_file')
        ```
        
        This will give us back a list of dictionaries, with each item in the list representing the modified data for each row in the initial csv file.
        
        We can also get the result fed back to us in csv format as a string
        
        ```python
        result = csv_converter.convert('path/to/csv/file', to='csv')
        ```
        
        ## Usage
        
        ### CLI
        
        ```bash
        csv-etl --config=./examples/order_data/config.yaml --csv=./examples/order_data/test_data.csv
        
        $ csv-etl --help
        Usage: csv-etl [OPTIONS]
        
        Options:
          --config   File path to the yaml configuration
          --csv      File path to the csv to convert
          --outfile  File path to write the result to
          --format   File path to the csv to convert
          --help     Show this message and exit.
        ```
        
        ### Examples
        
        More detailed usage and programmatic examples are provided
        
         - [Intro Example](https://github.com/winslowdibona/csv-etl/tree/master/examples/intro)
         - [Order Data Example](https://github.com/winslowdibona/csv-etl/tree/master/examples/order_data)
        
        ## Helpful Make Commands
        
        ### Generating Documentation
        
        ```bash
        make docs
        open html/csv_etl/index.html
        ```
        
        ### Running Tests
        
        ```bash
        make pytest
        ```
        
        ### Getting Test Coverage
        
        ```bash
        make test-cov
        open cov_html/index.html
        ```
        
        ## What Next?
        
        ### Better error handling
        
        Currently the errors are just printed to the console. Might be nice to have an option to gather them and have them represented in the resulting data set somehow.
        
        ### Better handling of eval statements
        
        Right now the use of `eval` statements is a little tailored for the original problem
        and could use some more exploration on how they can be further utilized and how to handle
        potential errors.
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
