Extract, Transform, and Load eBay Data in Python

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

The CData Python Connector for eBay enables you to create ETL applications and pipelines for eBay data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for eBay and the petl framework, you can build eBay-connected applications and pipelines for extracting, transforming, and loading eBay data. This article shows how to connect to eBay with the CData Python Connector and use petl and pandas to extract, transform, and load 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 driver 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.

After installing the CData eBay Connector, follow the procedure below to install the other required modules and start accessing eBay through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for eBay Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.ebay as mod

You can now connect with a connection string. Use the connect function for the CData eBay Connector to create a connection for working with eBay data.

cnxn = mod.connect("AppId=MyAppId;CertId=MyCertId;DevId=MyDevId;SiteId=MySiteId;RuName=MyRuName;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query eBay

Use SQL to create a statement for querying eBay. In this article, we read data from the ItemListing entity.

sql = "SELECT Title, HitCount FROM ItemListing WHERE ListingStatus = 'active'"

Extract, Transform, and Load the eBay Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the eBay data. In this example, we extract eBay data, sort the data by the HitCount column, and load the data into a CSV file.

Loading eBay Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'HitCount')


In the following example, we add new rows to the ItemListing table.

Adding New Rows to eBay

table1 = [ ['Title','HitCount'], ['NewTitle1','NewHitCount1'], ['NewTitle2','NewHitCount2'], ['NewTitle3','NewHitCount3'] ]

etl.appenddb(table1, cnxn, 'ItemListing')

With the CData Python Connector for eBay, you can work with eBay data just like you would with any database, including direct access to data in ETL packages like petl.

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Full Source Code

import petl as etl
import pandas as pd
import cdata.ebay as mod

cnxn = mod.connect("AppId=MyAppId;CertId=MyCertId;DevId=MyDevId;SiteId=MySiteId;RuName=MyRuName;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Title, HitCount FROM ItemListing WHERE ListingStatus = 'active'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'HitCount')


table3 = [ ['Title','HitCount'], ['NewTitle1','NewHitCount1'], ['NewTitle2','NewHitCount2'], ['NewTitle3','NewHitCount3'] ]

etl.appenddb(table3, cnxn, 'ItemListing')