{
    "info": {
        "author": "Oleh Omelchenko",
        "author_email": "ptrvtch@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
        ],
        "description": "# gaapi4py\n\nGoogle Analytics Reporting API v4 for Python 3\n\n## Prerequisites\n\nTo use this library, you need to have a project in Google Cloud Platform and a service account key that has access to Google Analytics account you want to get data from.\n\n## Quick Start\n\n```python\nfrom gaapi4py import GAClient\n# if GOOGLE_APPLICATION_CREDENTIALS is set:\nc = GAClient() \n# or you may specify keyfile path:\nc = GAClient(json_keyfile=\"path/to/keyfile.json\")\n\n\nrequest_body = {\n    'view_id': '123456789',\n    'start_date': '2019-01-01',\n    'end_date': '2019-01-31',\n    'dimensions': {\n        'ga:sourceMedium',\n        'ga:date'\n    },\n    'metrics': {\n        'ga:sessions'\n    },\n    'filter': 'ga:sourceMedium==google / organic' # optional filter clause\n}\n\nresponse = c.get_all_data(request_body)\n\nresponse['info'] # sampling and \"golden\" metadata\n\nresponse['data'] # Pandas dataframe that contains data from GA\n```\n\nIf you want to make many requests to a speficic view or with specific dateranges, you can set date ranges for all future requests:\n\n```python\n# Pass arguments to class init\nc = GAClient(view_id=\"123456789\", start_date=\"2019-09-01\", end_date=\"2019-09-07\") \n# or use methods to overwrite viewID or dateranges\nc.set_view_id('123456789')\nc.set_dateranges('2019-01-01', '2019-01-31')\n\nrequest_body_1 = {\n    'dimensions': {\n        'ga:sourceMedium',\n        'ga:date'\n    },\n    'metrics': {\n        'ga:sessions'\n    }\n}\n\nrequest_body_2 = {\n    'dimensions': {\n        'ga:deviceCategory',\n        'ga:date'\n    },\n    'metrics': {\n        'ga:sessions'\n    }\n}\n\nresponse_1 = c.get_all_data(request_body_1)\nresponse_2 = c.get_all_data(request_body_2)\n```\n\n## Avoid sampling by taking data day-by-day\n\n>Important! Google Analytics reporting API has a limit of maximum 100 requests per 100 seconds. If you want to iterate over large period of days, you might consider adding `time.sleep(1)` at the end of the loop to avoid reaching this limit.\n\n```python\nfrom datetime import date, timedelta\nfrom time import sleep\n\nimport pandas as pd\nfrom gaapi4py import GAClient\n\nc = GAClient(view_id='123456789')\n\nstart_date = date(2019,7,1)\nend_date = date(2019,7,14)\n\ndf_list = []\niter_date = start_date\nwhile iter_date <= end_date:\n    c.set_dateranges(iter_date, iter_date)\n    response = c.get_all_data({\n        'dimensions': {\n            'ga:sourceMedium',\n            'ga:deviceCategory'\n        },\n        'metrics': {\n            'ga:sessions'\n        }\n    })\n    df = response['data']\n    df['date'] = iter_date\n    df_list.append(response['data'])\n    iter_date = iter_date + timedelta(days=1)\n    time.sleep(1)\n\nall_data = pd.concat(df_list, ignore_index=True)\n\n```\n\n## Avoid \"maximum 7 dimensions\" restriction\n\nIf you store sessionId and/or hitId as custom dimensions ([Example implementation on Simo Ahava's blog](https://www.simoahava.com/analytics/improve-data-collection-with-four-custom-dimensions/)), you can circumvent restriction on maximum number of dimensions and metrics in one report. Example below:\n\n> If sampling starts to appear, try to break the set of dimensions into smaller parts and run queries on them.\n\n```python\none_day = date(2019,7,1)\nc.set_dateranges(one_day, one_day)\n\nSESSION_ID_CD_INDEX = '2'\nHIT_ID_CD_INDEX = '5'\n\nsession_id = 'dimension' + SESSION_ID_CD_INDEX\nhit_id = 'dimension' + HIT_ID_CD_INDEX\n\n\n#Get session scope data\nresponse_1 = c.get_all_data({\n    'dimensions': {\n        'ga:' + session_id,\n        'ga:sourceMedium',\n        'ga:campaign',\n        'ga:keyword',\n        'ga:adContent',\n        'ga:userType',\n        'ga:deviceCategory'\n    },\n    'metrics': {\n        'ga:sessions'\n    }\n})\n\nresponse2 = c.get_all_data({\n    'dimensions': {\n        'ga:' + session_id,\n        'ga:landingPagePath',\n        'ga:secondPagePath',\n        'ga:exitPagePath',\n        'ga:pageDepth',\n        'ga:daysSinceLastSession',\n        'ga:sessionCount'\n    },\n    'metrics': {\n        'ga:hits',\n        'ga:totalEvents',\n        'ga:bounces',\n        'ga:sessionDuration'\n    }\n})\n\nall_data = response_1['data'].merge(response2['data'], on=session_id, how='left')\n\nall_data.rename(index=str, columns={\n    session_id: 'session_id'\n}, inplace=True)\n\nall_data.head()\n\n# Get hit scope data\nhits_response_1 = c.get_all_data({\n    'dimensions': {\n        'ga:' + session_id,\n        'ga:' + hit_id,\n        'ga:pagePath',\n        'ga:previousPagePath',\n        'ga:dateHourMinute'\n    },\n    'metrics': {\n        'ga:hits',\n        'ga:totalEvents',\n        'ga:pageviews'\n    }\n})\n\nhits_response_2 = c.get_all_data({\n    'dimensions': {\n        'ga:' + session_id,\n        'ga:' + hit_id,\n        'ga:eventCategory',\n        'ga:eventAction',\n        'ga:eventLabel'\n    },\n    'metrics': {\n        'ga:totalEvents'\n    }\n})\n\nall_hits_data = hits_response_1['data'].merge(hits_response_2['data'],\n                                              on=[session_id, hit_id],\n                                              how='left')\nall_hits_data.rename(index=str, columns={\n    session_id: 'session_id',\n    hit_id: 'hit_id'\n}, inplace=True)\n\nall_hits_data.head()\n\n```\n\n\n",
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