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

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

Connecting to Teamgate Data

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

Teamgate API Profile Settings

To obtain your API Key, log into Teamgate and navigate to Settings > Additional Features > External Apps > New API Key Request. For your Auth Token, go to My Profile > Integrations > API Access > Auth Token.

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

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

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

Execute SQL to Teamgate

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, Status FROM Activities WHERE Status = 'Completed'", engine)

Visualize Teamgate Data

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

df.plot(kind="bar", x="Id", y="Status")
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 Teamgate 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\Teamgate.apip&ProfileSettings='APIKey=your_api_key&AuthToken=your_auth_token'")
df = pandas.read_sql("SELECT Id, Status FROM Activities WHERE Status = 'Completed'", engine)

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