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        "author": "Zhengyang Tang",
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        "description": "## Home\nBuilt on popular scientific computing toolkits, aresy aims to make it easy to process large-scale data on a single server. aresy treats the whole data flow as a computation graph, where the input data will be put into placeholders, and you can perform nearly any python-based transformation by extending the graph. aresy was designed with the following principles:\n\n- Compatible with python data science ecosystem, such as numpy, pytorch, tensorflow modules.\n- Able to process cross-type, cross-domain, cross-granularity data simultaneously.\n- Easy to build a large feature generator, a transformer or even a data loader to feed preprocessed data into deep learning models.\n\n## Installation\n```shell\npython setup.py install\n```\n\n## Package Reference\n\n- aresy.formatting\n    - aresy.formatting.TSVDataLoader\n    - aresy.formatting.TSVPlaceholder\n\n- aresy.preprocessing\n    - aresy.preprocessing.WrangleFloat\n    - aresy.preprocessing.WrangleStr\n    - aresy.preprocessing.WrangleNormalDatetime\n\n- aresy.aggregating\n    - aresy.aggregating.Sum\n    - aresy.aggregating.Mean\n    - aresy.aggregating.Std\n    - aresy.aggregating.Max\n    - aresy.aggregating.Min\n    - aresy.aggregating.Quantile\n\n- aresy.utils\n    - aresy.wrap\\_outputs\\_to\\_json\n",
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