pyga4’s documentation!
- class pyga4.model.bigquery.BaseTable(client, project_id: str, dataset_name: str)[source]
Bases:
ClientConnect to the data set from bigquery
- property all_tables_list: list
Return all tables id from data set
- property table_id: str
- class pyga4.model.bigquery.Client(client, project_id: str)[source]
Bases:
objectInstance the bigquery client and define its attributes
Parameters
- query_config:
if dry_run=True, the query will not response results, but return the bytes usage which indicate cost.
Examples
`python Client().query_config.dry_run = True `- query_config = <google.cloud.bigquery.job.query.QueryJobConfig object>
- class pyga4.model.bigquery.Ga4Table(client, project_id: str, dataset_name: str)[source]
Bases:
BaseTable- property device_category_list: list
Return all device_category from data table ex: mobile, desktop
- property device_language_list: list
Return all device_language from data table
- property device_mobile_brand_name_list: list
Return all device_mobile_brand_name from data table ex: Apple
- property device_mobile_model_name_list: list
Return all device_mobile_model_name from data table ex: iPhone
- property device_operating_system_list: list
Return all device_operating_system from data table ex: iOS
- property device_web_info_browser_list: list
Return all device_web_info_browser from data table
- property event_date_list: list
Return all event_date from data table
- property event_name_list: list
Return all event_name from data table
- property event_timestamp_list: list
Return all event_timestamp from data table
- property geo_city_list: list
Return all user city from data table
- property geo_country_list: list
Return all user countries from data table
- property geo_metro_list: list
Return all user metro from data table
- property geo_region_list: list
Return all user region from data table
- property geo_sub_continent_list: list
Return all user sub_continent from data table
- property page_location_list: list
Return all event of page location from data table
- property user_id_list: list
Return all user_id from data table
- class pyga4.analytic.analytic.DeviceAnalytic(table: Ga4Table)[source]
Bases:
BaseAnalyticThe features of technology
- property browser_distribution: Counter
- property category_distribution: Counter
- property mobile_brand_distribution: Counter
- property mobile_model_distribution: Counter
- property os_distribution: Counter
- class pyga4.analytic.analytic.EventAnalytic(table: Ga4Table)[source]
Bases:
BaseAnalyticThe features of event
- static conversion_rate(engagement_events: Any, target_events: Any) float[source]
Calculate the conversion rate
If your “target” is to measure how many people place an order`(purchased), so your `ad clicks would be the “engagement” because the orders resulting from the ad are your “conversions.”
If your “target” is to measure how many people clicking the ad, then your ad clicks would become the “conversions,” and ad impressions would become the “engagement.”
Parameters
- action_events:
The engagement events that users perform.
- target_events:
The target events that represent successful conversions.
Returns
- float
The calculated conversion rate as a percentage.
Examples
If action_events contains 1000 page views and target_events contains 200 purchases, the conversion rate would be calculated as:
>>> conversion_rate([...,1000], [...,200]) 20.0
If you have multiple engagement events and target events, you can calculate the combined conversion rate as well:
>>> conversion_rate([1000, 500], [200, 100]) 18.18
Note that the input lists should contain counts or frequencies of events.
- property pages_distribution: Counter
Return most common pages for all users