How to Build an ETL App for PayPal Data in Python with CData

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

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

Connecting to PayPal Data

Connecting to PayPal 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.

The provider surfaces tables from two PayPal APIs. The APIs use different authentication methods.

• The REST API uses the OAuth standard. To authenticate to the REST API, set the OAuthClientId, OAuthClientSecret, and CallbackURL properties.
• The Classic API requires Signature API credentials. To authenticate to the Classic API, obtain an API username, password, and signature.

See the "Getting Started" chapter of the help documentation for a guide to obtaining the necessary API credentials.

To select the API you want to work with, you can set the Schema property to REST or SOAP. By default the SOAP schema will be used.

For testing purposes you can set UseSandbox to true and use sandbox credentials.

After installing the CData PayPal Connector, follow the procedure below to install the other required modules and start accessing PayPal 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 PayPal 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.paypal as mod

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

cnxn = mod.connect("Schema=SOAP;Username=sandbox-facilitator_api1.test.com;Password=xyz123;Signature=zx2127;InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query PayPal

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

sql = "SELECT Date, GrossAmount FROM Transactions WHERE TransactionClass = 'Received'"

Extract, Transform, and Load the PayPal Data

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

Loading PayPal Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

etl.tocsv(table2,'transactions_data.csv')

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

Free Trial & More Information

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

---

Full Source Code

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

cnxn = mod.connect("Schema=SOAP;Username=sandbox-facilitator_api1.test.com;Password=xyz123;Signature=zx2127;InitiateOAuth=GETANDREFRESH;")

sql = "SELECT Date, GrossAmount FROM Transactions WHERE TransactionClass = 'Received'"

table1 = etl.fromdb(cnxn,sql)

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

etl.tocsv(table2,'transactions_data.csv')