Use pandas to Visualize Raisers Edge NXT Data in Python

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Raisers Edge NXT Python Connector

Python Connector Libraries for Raisers Edge NXT Data Connectivity. Integrate Raisers Edge NXT with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



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

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

Connecting to Raisers Edge NXT Data

Connecting to Raisers Edge NXT 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.

Before establishing a connection, supply the SubscriptionKey, found in the Blackbaud Raiser's Edge NXT Profile.

Authenticating to Raiser's Edge NXT

Blackbaud Raiser's Edge NXT uses the OAuth authentication standard. You can connect to without setting any connection properties using the embedded OAuth credentials.

Alternatively, you can authenticate by creating a custom app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties.

See the Help documentation for an authentication guide.

Follow the procedure below to install the required modules and start accessing Raisers Edge NXT 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 Raisers Edge NXT Data in Python

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

engine = create_engine("raiseredgenxt:///?SubscriptionKey=MySubscriptionKey&OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Raisers Edge NXT

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, AddressLines FROM Constituents WHERE Type = 'Home'", engine)

Visualize Raisers Edge NXT Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the Raisers Edge NXT Python Connector to start building Python apps and scripts with connectivity to Raisers Edge NXT 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("raiseredgenxt:///?SubscriptionKey=MySubscriptionKey&OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Id, AddressLines FROM Constituents WHERE Type = 'Home'", engine)

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