Use pandas to Visualize Xero WorkflowMax Data in Python

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Xero WorkflowMax Python Connector

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



The CData Python Connector for Xero WorkflowMax enables you use pandas and other modules to analyze and visualize live Xero WorkflowMax 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 Xero WorkflowMax, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Xero WorkflowMax-connected Python applications and scripts for visualizing Xero WorkflowMax data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Xero WorkflowMax data, execute queries, and visualize the results.

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

Connecting to Xero WorkflowMax Data

Connecting to Xero WorkflowMax 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 to the WorkflowMax API, obtain an APIKey and AccountKey from Xero. This can only be done by contacting Xero support (https://www.workflowmax.com/contact-us).

After obtaining an API Key and Account Key, set the values in the APIKey and AccountKey connection properties. Once these are set, you are ready to connect.

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

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

engine = create_engine("xeroworkflowmax:///?APIKey=myApiKey&AccountKey=myAccountKey")

Execute SQL to Xero WorkflowMax

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, Name FROM Clients WHERE Name = 'Cynthia'", engine)

Visualize Xero WorkflowMax Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the Xero WorkflowMax Python Connector to start building Python apps and scripts with connectivity to Xero WorkflowMax 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("xeroworkflowmax:///?APIKey=myApiKey&AccountKey=myAccountKey")
df = pandas.read_sql("SELECT Id, Name FROM Clients WHERE Name = 'Cynthia'", engine)

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