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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 Workday Python Connector 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()