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        "author": "Nader Bedretdinov",
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        "description": "# Phouse easy work with clickhouse \n\n\nhttps://github.com/bedretdinov/phouse  \n\n# for install\n```python\npip3 install git+https://github.com/bedretdinov/phouse\n```\n\n\n\n# Examples\n```python\n    \n    from phouse.phouse import Phouse \n    \n    \n    # The connection will be repeat if get failed. It is need for stability your ETL scripts.\n    # The script will try until get connection. try with interval 60 sec\n    pd = Phouse.getConnection({\n        'host':'0.0.0.0',\n        'user':'default',\n        'password':'',\n        'database':'owox'\n    })\n      \n    # return pandas dataframe\n    df = pd.clickhouse_query(''' SELECT database, name FROM system.tables ''')\n    \n    # !!!! And very important things this data frame must have date column else you get error\n    df['date'] = pd.to_datetime('today')\n    \n    # insert to the table\n    df.clickhouse.append(table='new_table') # The table is created automatically\n    \n    # truncate table and insert\n    df.clickhouse.write(table='new_table') # The table is created automatically \n    \n    \n    # write by package\n    \n    from sklearn import datasets \n    \n    iris = datasets.load_iris() \n    \n    df = pd.DataFrame(iris.data, columns=iris.feature_names)\n    df['date'] = pd.to_datetime('today')\n    \n    first = True\n    buffer_size = 10\n    for i in range(0,df.shape[0],buffer_size):\n        item = df[i:i+buffer_size]  \n        if(first):\n            item.clickhouse.write(table='iris_test')\n            first = not first \n        else:\n            item.clickhouse.append(table='iris_test')\n            \n        \n        \n        \n        \n# How to create engine and partition\n\n\n\n    # VersionedCollapsingMergeTree \n    \n    df = pd.DataFrame([\n        ['Y',0.4,1, 56],\n        ['N',0.6,-1,34]\n    ], columns=['status','probability','category','party'])\n    \n    \n    df.clickhouse.drop(table='test333')\n    \n    df.clickhouse.VersionedCollapsingMergeTree(\n        SIGN = 'category',\n        VERSION = 'party',\n        PARTITION = 'status', \n        ORDER_BY = 'status',\n        INDEX_GRANULARITY = 8192\n    ) \n    \n    df.clickhouse.append(table='test333')     \n        \n# __________________________________________________________________\n\n    # CollapsingMergeTree\n    \n    df = pd.DataFrame([\n        ['Y',0.4,1],\n        ['N',0.6,-1]\n    ], columns=['status','probability','category'])\n    \n    \n    df.clickhouse.drop(table='test333')\n    \n    df.clickhouse.CollapsingMergeTree(\n        SIGN = 'category',\n        PARTITION = 'status', \n        ORDER_BY = 'status',\n        INDEX_GRANULARITY = 8192\n    ) \n    \n    df.clickhouse.append(table='test333')\n    \n# __________________________________________________________________\n\n    # CollapsingMergeTree\n\n    df = pd.DataFrame([\n        ['Y',0.4,1],\n        ['N',0.6,-1]\n    ], columns=['status','probability','category'])\n    \n    \n    df.clickhouse.drop(table='test333')\n    \n    df.clickhouse.CollapsingMergeTree(\n        SIGN = 'category',\n        PARTITION = 'status', \n        ORDER_BY = 'status',\n        INDEX_GRANULARITY = 8192\n    ) \n    \n    df.clickhouse.append(table='test333')\n    \n# __________________________________________________________________\n    \n    # AggregatingMergeTree\n    \n    \n    df = pd.DataFrame([\n        ['Y',0.4,56],\n        ['N',0.6,23]\n    ], columns=['status','probability','category'])\n    \n    \n    df.clickhouse.drop(table='test333')\n    \n    df.clickhouse.AggregatingMergeTree(\n        PARTITION = 'status', \n        ORDER_BY = 'status',\n        INDEX_GRANULARITY = 8192\n    ) \n    \n    df.clickhouse.append(table='test333')\n    \n# __________________________________________________________________\n\n    # Clean table\n    df.clickhouse.truncate(table='test333')\n    \n\n```",
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