Use pandas to Visualize PayPal Data in Python

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PayPal Python Connector

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



The CData Python Connector for PayPal enables you use pandas and other modules to analyze and visualize live PayPal data in Python.

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, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build PayPal-connected Python applications and scripts for visualizing PayPal data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to PayPal data, execute queries, and visualize the results.

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, you will need to set the OAuthClientId, OAuthClientSecret, and CallbackURL properties.
  • The Classic API requires Signature API credentials. To authenticate to the Classic API, you will need to 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.

Follow the procedure below to install the required modules and start accessing PayPal through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize PayPal Data in Python

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

engine = create_engine("paypal:///?Schema=SOAP&Username=sandbox-facilitator_api1.test.com&Password=xyz123&Signature=zx2127&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to PayPal

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT Date, GrossAmount FROM Transactions WHERE TransactionClass = 'Received'", engine)

Visualize PayPal Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the PayPal data. The show method displays the chart in a new window.

df.plot(kind="bar", x="Date", y="GrossAmount")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the PayPal Python Connector 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 pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("paypal:///?Schema=SOAP&Username=sandbox-facilitator_api1.test.com&Password=xyz123&Signature=zx2127&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Date, GrossAmount FROM Transactions WHERE TransactionClass = 'Received'", engine)

df.plot(kind="bar", x="Date", y="GrossAmount")
plt.show()