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