How to Visualize Jumpseller 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 Jumpseller-connected Python applications and scripts for visualizing Jumpseller data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Jumpseller data, execute queries, and visualize the results.

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

Connecting to Jumpseller Data

Connecting to Jumpseller 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.

Start by setting the Profile connection property to the location of the Jumpseller Profile on disk (e.g. C:\profiles\Jumpseller.apip). Next, set the ProfileSettings connection property to the connection string for Jumpseller (see below).

Jumpseller API Profile Settings

In your Jumpseller store admin panel, navigate to Apps > Developers > Add Application to register an OAuth app and obtain a Client ID and Client Secret.

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

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

engine = create_engine("api:///?Profile=C:\profiles\Jumpseller.apip&Authscheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url")

Execute SQL to Jumpseller

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

df = pandas.read_sql("SELECT Code, Name FROM Apps WHERE Author = 'Jumpseller'", engine)

Visualize Jumpseller Data

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

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

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 Jumpseller 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("api:///?Profile=C:\profiles\Jumpseller.apip&Authscheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url")
df = pandas.read_sql("SELECT Code, Name FROM Apps WHERE Author = 'Jumpseller'", engine)

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