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        "author": "David Riedel",
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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Utilities"
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        "description": "# resumableds\nA Python class that supports Data Science projects.\n\nresumableds supports you in writing data science scripts including save/resume functionality.\n\nData can be saved and resumed avoiding unnessary retrievals of raw data from data storages.\n\nThe data directory structure is inspired by cookiecutter-data-science (https://drivendata.github.io/cookiecutter-data-science/).\n\nThe class also supports the statement 'Analysis is a DAG' (https://drivendata.github.io/cookiecutter-data-science/#analysis-is-a-dag).\n\n\nresumableds is written in pure Python and it is intended to be used within Jupyter notebooks.\nIt however can also be useful in Python scripts or script pipelines.\n\n### Example\n\n```\nproj1 = RdsProject('project1') # create object from class (creates the dir if it doesn't exist yet)\nproj1.raw.df1 = pd.DataFrame() # create dataframe as attribute of proj1.raw (RdsFs 'raw')\nproj1.defs.variable1 = 'foo' # create simple objects as attribute of proj1.defs (RdsFs 'defs')\nproj1.save() # saved attributes of all RfdFs in proj1 to disk\n```\n\nThis will result in the following directory structure (plus some overhead of internals):\n\n- <output_dir>/defs/var_variable1.pkl\n- <output_dir>/raw/df1.pkl\n- <output_dir>/raw/df1.csv\n\nNote, pandas dataframes are always dumped as pickle for further processing and as csv for easy exploration. The csv files are never read back anymore.\n\nLater on or in another python session, you can do this:\n\n```\nproj2 = RdsProject('project1') # vars and data are read back to their original names\nproj2.defs.variable1 == 'foo' # ==> True\nisinstance(proj2.raw.df1, pd.DataFrame) # ==> True\n```\n",
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