Metadata-Version: 2.1
Name: viadot
Version: 0.2.2
Summary: A simple data ingestion library to guide data flows from some places to other places
Home-page: https://github.com/dyvenia/viadot
Author: Alessio Civitillo
License: UNKNOWN
Description: # Viadot
        [![build status](https://github.com/dyvenia/viadot/actions/workflows/build.yml/badge.svg)](https://github.com/dyvenia/viadot/actions/workflows/build.yml)
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        ---
        
        **Documentation**: <a href="https://dyvenia.github.io/viadot/" target="_blank">https://dyvenia.github.io/viadot/</a>
        
        **Source Code**: <a href="https://github.com/dyvenia/viadot" target="_blank">https://github.com/dyvenia/viadot</a>
        
        ---
        
        A simple data ingestion library to guide data flows from some places to other places.
        
        ## Getting Data from a Source
        
        Viadot supports several API and RDBMS sources, private and public. Currently, we support the UK Carbon Intensity public API and base the examples on it.
        
        ```python
        from viadot.sources.uk_carbon_intensity import UKCarbonIntensity
        ukci = UKCarbonIntensity()
        ukci.query("/intensity")
        ukci.to_df()
        ```
        
        The above code pulls data from the API to a pandas `DataFrame`.
        
        ## Loading Data to a Source
        Depending on the source, viadot provides different methds of uploading data.
        For instance, for SQL sources, this would be bulk inserts. For data lake sources, it would be
        a file upload. We also provide ready-made pipelines including data valiadation steps using
        Great Expectations.
        
        An example of loading data into SQLite from a pandas `DataFrame` using the `SQLiteInsert` task:
        
        ```python
        from viadot.tasks import SQLiteInsert
        
        insert_task = SQLiteInsert()
        insert_task.run(table_name=TABLE_NAME, dtypes=dtypes, db_path=database_path, df=df, if_exists="replace")
        ```
        
        
        ## Running tests
        To run tests, log into the container and run pytest:
        ```
        cd viadot/docker
        run.sh
        docker exec -it viadot_testing bash
        pytest
        ```
        
        ## Running flows locally
        You can run the example flows from the terminal:
        ```
        run.sh
        docker exec -it viadot_testing bash
        FLOW_NAME=hello_world; python -m viadot.examples.$FLOW_NAME
        ```
        
        However, when developing, the easiest way is to use the provided Jupyter Lab container available at `http://localhost:9000/`.
        
        
        ## How to contribute
        1. Clone the release branch 
        2. Pull the docker env by running `viadot/docker/update.sh`
        3. Run the env with `viadot/docker/run.sh`
        4. Log into the dev container and install viadot in development mode: 
        ```
        docker exec -it viadot_testing bash
        pip install -e .
        ```
        5. Edit and test your changes with `pytest`
        6. Submit a PR. The PR should contain the following:
        - new/changed functionality
        - tests for the changes
        - changes added to `CHANGELOG.md`
        - any other relevant resources updated (esp. `viadot/docs`)
        
        Please follow the standards and best practices used within the library (eg. when adding tasks, see how other tasks are constructed, etc.). For any questions,
        please reach out to us here on GitHub.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
