How to Visualize Paddle Data in Python with pandas

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Paddle-connected Python applications and scripts for visualizing Paddle data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Paddle data, execute queries, and visualize the results.

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

Connecting to Paddle Data

Connecting to Paddle 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.

Using API Key Authentication

Paddle uses API key authentication. To obtain an API key:

1. Sign in to your Paddle account at https://vendors.paddle.com
2. Navigate to Developer Tools > Authentication
3. Click "Generate API Key"
4. Assign the appropriate permissions for the data you wish to access
5. Copy the generated key (sandbox keys begin with pdl_sdbx_apikey_; production keys begin with pdl_live_apikey_)

After obtaining your API key, set the following connection properties:

• AuthScheme: Set this to APIKey.

Set the following in the ProfileSettings connection property:

• APIKey: Set this to your Paddle API key.

Example Connection String

Profile=C:\profiles\Paddle.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key";

Connecting to Paddle

Once the authentication is configured, you can connect to Paddle and query data from any of the available tables such as Products, Customers, Subscriptions, and Transactions.

Follow the procedure below to install the required modules and start accessing Paddle 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 Paddle Data in Python

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

engine = create_engine("api:///?Profile=C:\profiles\Paddle.apip&AuthScheme=APIKey&ProfileSettings="APIKey=your_api_key"")

Execute SQL to Paddle

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

df = pandas.read_sql("SELECT , FROM Products WHERE = ''", engine)

Visualize Paddle Data

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

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

👁 Paddle data in a Python plot (Salesforce is shown).

Free Trial & More Information

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

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Full Source Code

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

engine = create_engine("api:///?Profile=C:\profiles\Paddle.apip&AuthScheme=APIKey&ProfileSettings="APIKey=your_api_key"")
df = pandas.read_sql("SELECT , FROM Products WHERE = ''", engine)

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