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

How to Visualize Sugar CRM Data in Python with pandas

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

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

Connecting to Sugar CRM Data

Connecting to Sugar CRM 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.

The User and Password properties, under the Authentication section, must be set to valid SugarCRM user credentials. This will use the default OAuth token created to allow client logins. OAuthClientId and OAuthClientSecret are required if you do not wish to use the default OAuth token.

You can generate a new OAuth consumer key and consumer secret in Admin -> OAuth Keys. Set the OAuthClientId to the OAuth consumer key. Set the OAuthClientSecret to the consumer secret.

Additionally, specify the URL to the SugarCRM account.

Note that retrieving SugarCRM metadata can be expensive. It is advised that you store the metadata locally as described in the "Caching Metadata" chapter of the help documentation.

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

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

engine = create_engine("sugarcrm:///?User=MyUser&Password=MyPassword&URL=MySugarCRMAccountURL&CacheMetadata=True")

Execute SQL to Sugar CRM

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, AnnualRevenue FROM Accounts WHERE Name = 'Bob'", engine)

Visualize Sugar CRM Data

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

df.plot(kind="bar", x="Name", y="AnnualRevenue")

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Sugar to start building Python apps and scripts with connectivity to Sugar CRM 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("sugarcrm:///?User=MyUser&Password=MyPassword&URL=MySugarCRMAccountURL&CacheMetadata=True")
df = pandas.read_sql("SELECT Name, AnnualRevenue FROM Accounts WHERE Name = 'Bob'", engine)

df.plot(kind="bar", x="Name", y="AnnualRevenue")