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Get the Report →Use pandas to Visualize Workday Data in Python
The CData Python Connector for Workday enables you use pandas and other modules to analyze and visualize live Workday 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 Workday, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Workday-connected Python applications and scripts for visualizing Workday data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Workday data, execute queries, and visualize the results.
With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Workday data in Python. When you issue complex SQL queries from Workday, the driver pushes supported SQL operations, like filters and aggregations, directly to Workday and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to Workday Data
Connecting to Workday 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.
To connect, there are three pieces of information required: Authentication, API URL, and WSDL URL.
Authentication
To authenticate, specify your User and Password. Note that you must append your Tenant to your User separated by an '@' character. For instance, if you normally log in with 'geraldg' and your Tenant is 'mycompany_mc1', then your User should be specified as 'geraldg@mycompany_mc1'.
API URL
The API URL may be specified either directly via APIURL, or it may be constructed from the Tenant, Service, and Host. The APIURL is constructed in the following format: <Host>/ccx/service/<Tenant>/<Service>.
WSDL URL
The WSDLURL may be specified in its entirety, or may be constructed from the Service and WSDLVersion connection properties. The WSDLURL is constructed in the following format: https://community.workday.com/sites/default/files/file-hosting/productionapi/<Service>/<WSDLVersion>/<Service>.wsdl
Follow the procedure below to install the required modules and start accessing Workday 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 Workday Data in Python
You can now connect with a connection string. Use the create_engine function to create an Engine for working with Workday data.
engine = create_engine("workday:///?User=myuser&Password=mypassword&Tenant=mycompany_gm1&Host=https://wd3-impl-services1.workday.com")
Execute SQL to Workday
Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.
df = pandas.read_sql("SELECT Worker_Reference_WID, Legal_Name_Last_Name FROM Workers WHERE Legal_Name_Last_Name = 'Morgan'", engine)
Visualize Workday Data
With the query results stored in a DataFrame, use the plot function to build a chart to display the Workday data. The show method displays the chart in a new window.
df.plot(kind="bar", x="Worker_Reference_WID", y="Legal_Name_Last_Name") plt.show()
Free Trial & More Information
Download a free, 30-day trial of the CData Python Connector for Workday to start building Python apps and scripts with connectivity to Workday 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("workday:///?User=myuser&Password=mypassword&Tenant=mycompany_gm1&Host=https://wd3-impl-services1.workday.com") df = pandas.read_sql("SELECT Worker_Reference_WID, Legal_Name_Last_Name FROM Workers WHERE Legal_Name_Last_Name = 'Morgan'", engine) df.plot(kind="bar", x="Worker_Reference_WID", y="Legal_Name_Last_Name") plt.show()