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

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Use SQLAlchemy ORMs to Access eBay Data in Python


The CData Python Connector for eBay enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of eBay 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 and the SQLAlchemy toolkit, you can build eBay-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to eBay data to query, update, delete, and insert eBay data.

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

Connecting to eBay Data

Connecting to eBay 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.

If you will be accessing your own account, you can generate an OAuthAccessToken from your developer account dashboard. You can also allow other users to securely access their own accounts.

Both of these methods require you to create an application key set to obtain values for the following connection properties: AppId, CertId, DevId, and SiteId.

The user consent flow additionally requires the RuName and CallbackURL.

See the "Getting Started" chapter in the help documentation for a guide to using OAuth.

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

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker

Model eBay Data in Python

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

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("ebay:///?AppId=MyAppId&CertId=MyCertId&DevId=MyDevId&SiteId=MySiteId&RuName=MyRuName&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for eBay 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 ItemListing 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 ItemListing(base): __tablename__ = "ItemListing" Title = Column(String,primary_key=True) HitCount = Column(String) ...

Query eBay 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("ebay:///?AppId=MyAppId&CertId=MyCertId&DevId=MyDevId&SiteId=MySiteId&RuName=MyRuName&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(ItemListing).filter_by(ListingStatus="active"): print("Title: ", instance.Title) print("HitCount: ", instance.HitCount) 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

ItemListing_table = ItemListing.metadata.tables["ItemListing"] for instance in session.execute(ItemListing_table.select().where(ItemListing_table.c.ListingStatus == "active")): print("Title: ", instance.Title) print("HitCount: ", instance.HitCount) print("---------")

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

Insert eBay Data

To insert eBay data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to eBay.

new_rec = ItemListing(Title="placeholder", ListingStatus="active") session.add(new_rec) session.commit()

Update eBay Data

To update eBay data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to eBay.

updated_rec = session.query(ItemListing).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.ListingStatus = "active" session.commit()

Delete eBay Data

To delete eBay data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).

deleted_rec = session.query(ItemListing).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() session.delete(deleted_rec) session.commit()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for eBay to start building Python apps and scripts with connectivity to eBay data. Reach out to our Support Team if you have any questions.