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        "author": "Alessandro Berti",
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        "description": "# pm4pydask\n\nEfficient dataframe handling, for big files that cannot be stored directly in memory!\n\n\nTo load a Dask dataframe, the following instructions could be used:\n\nimport dask.dataframe as dd\ndf = dd.read_csv(\"C:\\\\roadtraffic.csv\")\n\n\nThen, the Directly-Follows Graph (albeit in a much slower way than the one provided by PM4Py routines) could be obtained using the following instruction:\n\ndfg = calculate_dfg.get_dfg_frequency(df)\n\n\nThe conversion from a huge CSV (path is provided) to efficient Parquet data storage (to be used with the fast routines in PM4Py :)) ) could be done through\n\nfrom pm4pydask.conversion.to_parquet import read_csv_path_to_parquet_directory\n\nread_csv_path_to_parquet_directory(\"<<path to CSV>>\", \"<<path to a folder that will contain the Parquet files>>\")\n\n",
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        "license": "GPL 3.0",
        "maintainer": "",
        "maintainer_email": "",
        "name": "pm4pydask",
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            "Tracker": "https://github.com/pm4py/pm4py-source/issues"
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        "summary": "Process Mining for Python - Dask module",
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