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    "info": {
        "author": "Chu-Hsuan Lee",
        "author_email": "joseph.chuhsuanlee@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
        ],
        "description": "# guacamoleETL - Document\n\nAn ETL pipeline tool that\n* pre-process\n  * extracts data from a .txt file ([Challenge_me.txt](Challenge_me.txt))\n  * cleans up the data with invalid information\n* transforms the data through given specifications into a matrix (list of lists)\n* loads the data into a .csv file ([output.csv](output.csv))\n\n## Installation\nThis tool can be installed with `pip`<br>\nCopy-paste and run this command in the terminal\n```\npip install guacamoleETL\n```\n\n## Usage\nThis ETL pipeline can be part of predictive model training and feed the data directly to the model\n```py\nimport guacamoleETL\n\ndataFile = 'Challenge_me.txt'\n\nguacamoleETL.load(dataFile)\nresult = guacamoleETL.transform(dataFile)\npredictive_model = model_training(result)\n```\n\n## Functions\n* __extract_data(txt_file):__\n  __Extract data from a .txt file to a temporary .csv file__<br>\n  Leading or trailing whitespace are removed during the extraction\n* __clean_up():__\n  __Clean up the data with invalid information__<br>\n  Rows with the placeholder '-' (NA) in any of the specified columns are excluded\n* __transform(path):__\n  __Transform the data from pre-process through given specifications into a matrix__<br>\n  `engine-location` is split into two columns, `engine-location_front` and `engine-location_rear` and one-hot-encoded<br>\n  `num-of-cylinders` is transformed from word into integer through a pre-defined dictionary<br>\n  `engine-size` is transformed into integer<br>\n  `weight` is transformed into integer<br>\n  `horsepower` is transformed from German decimal notation string into float number<br>\n  `aspiration` is modified as `aspiration_turbo` so that turbo engines are marked as 1<br>\n  `price` is converted from minor units to major units<br>\n  `make` is not transformed but kept in the dataset\n* __load(path):__\n  __Load the data from previous transformation into a .csv file__\n\n\n## Architecture\nAll the functions are implemented in the [\\_\\_init\\_\\_.py](guacamoleETL/__init__.py), this decision is made based on the following reasons:\n* After the package is imported, if we want to use the transform and load functions directly as sub-module, the functions must be imported or defined in `__init__.py`.\n* Since they are all connected to each other, such as the transform function takes the result from pre-process (extract and clean up) and the load function also takes the result from transform function, it's easier to follow the flow if they are all in the same file.\n* This might not be the best architecture implementation, but while starting from small, simplicity is always a good consideration.\n\n\n",
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        "name": "guacamoleETL",
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        "summary": "ETL package for AUTO1 challenge",
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