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

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

Connecting to Shortcut Data

Connecting to Shortcut 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 Shortcut Profile on disk (e.g. C:\profiles\Shortcut.apip). Next, set the ProfileSettings connection property to the connection string for Shortcut (see below).

Shortcut API Profile Settings

Log into your Shortcut account, navigate to Settings > API Tokens, and click Generate Token.

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

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

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

Execute SQL to Shortcut

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

df = pandas.read_sql("SELECT Id, Name FROM Categories WHERE IsArchived = 'false'", engine)

Visualize Shortcut Data

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

df.plot(kind="bar", x="Id", 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 Shortcut 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\Shortcut.apip&ProfileSettings='APIKey=your_api_key'")
df = pandas.read_sql("SELECT Id, Name FROM Categories WHERE IsArchived = 'false'", engine)

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