How to Visualize Xero 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 Xero, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Xero-connected Python applications and scripts for visualizing Xero data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Xero 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 data in Python. When you issue complex SQL queries from Xero, the driver pushes supported SQL operations, like filters and aggregations, directly to Xero and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Getting Started

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Connecting to Xero Data

Connecting to Xero 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, set the Schema connection property in addition to any authentication values. Xero offers authentication for private applications, public applications, and partner applications. You will need to set the XeroAppAuthentication property to PUBLIC, PRIVATE, or PARTNER, depending on the type of application configured. To connect from a private application, you will additionally need to set the OAuthAccessToken, OAuthClientId, OAuthClientSecret, CertificateStoreType, CertificateStore, and CertificateStorePassword.

To connect from a public or partner application, you can use the embedded OAuthClientId, OAuthClientSecret, and CallbackURL, or you can register an app to obtain your own OAuth values.

See the "Getting Started" chapter of the help documentation for a guide to authenticating to Xero.

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

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

engine = create_engine("xero:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Xero

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

df = pandas.read_sql("SELECT Name, QuantityOnHand FROM Items WHERE Name = 'Golf balls - white single'", engine)

Visualize Xero Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Xero to start building Python apps and scripts with connectivity to Xero 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("xero:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Name, QuantityOnHand FROM Items WHERE Name = 'Golf balls - white single'", engine)

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