{ "info": { "author": "Claudio Pose", "author_email": "claudio.pose@gmail.com", "bugtrack_url": null, "classifiers": [], "description": "analytics-data\n==============\n\nProvides an API wrapper for third party analytics services for easy data\nretrieval\n\nMain Features\n=============\n\n- Webtrekk JSON/RPC v1.1\n- Quintly REST v0.9\n- Credential manager\n\nQuintly API\n===========\n\nTo use the Quintly API through analytics-data, the QuintlyAPI class can\nbe used.\n\n.. code:: python\n\n # Import the module\n from analytics import quintly\n import datetime\n\n # Instantiate the class with your client id and secret\n quintly = quintly.QuintlyAPI('client_id', 'client_secret')\n\n # Available profiles are loaded after instantiation\n profiles = quintly.get_profiles()\n\n # Available groups are loaded after instantiation\n groups = quintly.get_groups()\n\n # To run the query, the profile ids are required. They can either be\n # retrieved through a group or by providing a list of profile names\n\n profile_ids_from_group = quintly.get_profile_ids_from_group_name('group_name')\n profile_ids_from_profile_names = quintly.get_profile_ids_from_names(['profile_name_1', 'profile_name_2'])\n\n\n # A query must specify the profiles for which the metrics should be retrieved...\n profile_ids = profile_ids_from_group\n\n #... the table from which the metrics should be retrieved...\n table = 'facebookInsights'\n\n #... the fields which are of interest...\n fields = ['profileId', 'time', 'page_impressions_unique']\n\n # ... a start and end date\n start_date = datetime.date(2019, 2, 1)\n end_date = datetime.date(2019, 2, 11)\n\n\n # You can run the query with the run_query method. It returns a pandas DataFrame\n df = quintly.run_query(profile_ids, table, fields, start_date, end_date)\n\n # The default interval is daily but can be changed using the interval parameter\n df = quintly.run_query(profile_ids, table, fields, start_date, end_date,\n interval='monthly')\n\n # If the query is too big due to too many profiles, the query can be split up\n # into subqueries by setting split_profiles to True -> for each profile one subquery\n\n df = quintly.run_query(profile_ids, table, fields, start_date, end_date,\n split_profiles=True)\n\n # If the query is too big due to a too large time range, the query can be split\n # into subqueries by setting the number of days of the split_days parameter\n # -> for each chunk with the size of the specified amount of days a subquery is run\n\n df = quintly.run_query(profile_ids, table, fields, start_date, end_date,\n split_days=28)\n\n # split_profiles and split_days can also be combined.\n\n\n", "description_content_type": "", 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