Use pandas to Visualize Bullhorn CRM Data in Python

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Bullhorn CRM Python Connector

Python Connector Libraries for Bullhorn CRM Data Connectivity. Integrate Bullhorn CRM with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for Bullhorn CRM enables you use pandas and other modules to analyze and visualize live Bullhorn CRM data in Python.

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

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

Connecting to Bullhorn CRM Data

Connecting to Bullhorn CRM 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.

Begin by providing your Bullhorn CRM account credentials in the following:

If you are uncertain about your data center code, codes like CLS2, CLS21, etc. are cluster IDs that are contained in a user's browser URL (address bar) once they are logged in.

Example: https://cls21.bullhornstaffing.com/BullhornSTAFFING/MainFrame.jsp?#no-ba... indicates that the logged in user is on CLS21.

Authenticating with OAuth

Bullhorn CRM uses the OAuth 2.0 authentication standard. To authenticate using OAuth, create and configure a custom OAuth app. See the Help documentation for more information.

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

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

engine = create_engine("bullhorncrm:///?DataCenterCode=CLS33&OAuthClientId=myoauthclientid&OAuthClientSecret=myoauthclientsecret&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Bullhorn CRM

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, CandidateName FROM Candidate WHERE CandidateName = 'Jane Doe'", engine)

Visualize Bullhorn CRM Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the Bullhorn CRM Python Connector to start building Python apps and scripts with connectivity to Bullhorn CRM 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("bullhorncrm:///?DataCenterCode=CLS33&OAuthClientId=myoauthclientid&OAuthClientSecret=myoauthclientsecret&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Id, CandidateName FROM Candidate WHERE CandidateName = 'Jane Doe'", engine)

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