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

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

Connecting to ServiceNow Data

Connecting to ServiceNow 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.

ServiceNow uses the OAuth 2.0 authentication standard. To authenticate using OAuth, you will need to register an OAuth app with ServiceNow to obtain the OAuthClientId and OAuthClientSecret connection properties. In addition to the OAuth values, you will need to specify the Instance, Username, and Password connection properties.

See the "Getting Started" chapter in the help documentation for a guide on connecting to ServiceNow.

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

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

engine = create_engine("servicenow:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&Username=MyUsername&Password=MyPassword&Instance=MyInstance&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to ServiceNow

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

df = pandas.read_sql("SELECT sys_id, priority FROM incident WHERE category = 'request'", engine)

Visualize ServiceNow Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for ServiceNow to start building Python apps and scripts with connectivity to ServiceNow 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("servicenow:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&Username=MyUsername&Password=MyPassword&Instance=MyInstance&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT sys_id, priority FROM incident WHERE category = 'request'", engine)

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