Use SQLAlchemy ORMs to Access eBay Analytics Data in Python

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eBay Analytics Python Connector

Python Connector Libraries for eBay Analytics Data Connectivity. Integrate eBay Analytics with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for eBay Analytics enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of eBay Analytics data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for eBay Analytics and the SQLAlchemy toolkit, you can build eBay Analytics-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to eBay Analytics data to query eBay Analytics data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live eBay Analytics data in Python. When you issue complex SQL queries from eBay Analytics, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to eBay Analytics and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to eBay Analytics Data

Connecting to eBay Analytics data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

You can authenticate to eBay Analytics only via the OAuth 2 authentication method. The eBay Analytics API requires an access token created with the authorization code grant flow to authorize the requests.

You can follow the guide in the Help documentation for a step by step guide on how to authenticate using the OAuth 2 protocol.

Follow the procedure below to install SQLAlchemy and start accessing eBay Analytics through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit:

pip install sqlalchemy

Be sure to import the module with the following:

import sqlalchemy

Model eBay Analytics Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with eBay Analytics data.

engine = create_engine("ebayanalytics:///?OAuthClientId=MyAppID&OAuthClientSecret=MyCertID&RuName=MyRuName&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for eBay Analytics Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the TrafficReportByListing table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base()
class TrafficReportByListing(base):
	__tablename__ = "TrafficReportByListing"
	ListingName = Column(String,primary_key=True)
	ClickThroughRate = Column(String)
	...

Query eBay Analytics Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("ebayanalytics:///?OAuthClientId=MyAppID&OAuthClientSecret=MyCertID&RuName=MyRuName&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(TrafficReportByListing).filter_by(ListingId="201284405428"):
	print("ListingName: ", instance.ListingName)
	print("ClickThroughRate: ", instance.ClickThroughRate)
	print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

TrafficReportByListing_table = TrafficReportByListing.metadata.tables["TrafficReportByListing"]
for instance in session.execute(TrafficReportByListing_table.select().where(TrafficReportByListing_table.c.ListingId == "201284405428")):
	print("ListingName: ", instance.ListingName)
	print("ClickThroughRate: ", instance.ClickThroughRate)
	print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Free Trial & More Information

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