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Use pandas to Visualize FinancialForce Data in Python

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

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

Connecting to FinancialForce Data

Connecting to FinancialForce 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.

There are several authentication methods available for connecting to FinancialForce: login credentials, SSO, and OAuth.

Authenticating with a Login and Token

Set the User and Password to your login credentials. Additionally, set the SecurityToken. By default, the SecurityToken is required, but you can make it optional by allowing a range of trusted IP addresses.

To disable the security token:

  1. Log in to FinancialForce and enter "Network Access" in the Quick Find box in the setup section.
  2. Add your IP address to the list of trusted IP addresses.

To obtain the security token:

  1. Open the personal information page on FinancialForce.com.
  2. Click the link to reset your security token. The token will be emailed to you.
  3. Specify the security token in the SecurityToken connection property or append it to the Password.

Authenticating with OAuth

If you do not have access to the user name and password or do not want to require them, use the OAuth user consent flow. See the OAuth section in the Help for an authentication guide.

Connecting to FinancialForce Sandbox Accounts

Set UseSandbox to true (false by default) to use a FinancialForce sandbox account. Ensure that you specify a sandbox user name in User.

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

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

engine = create_engine("financialforce:///?User=myUser&Password=myPassword&Security Token=myToken&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to FinancialForce

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

df = pandas.read_sql("SELECT BillingState, Name FROM Account WHERE Industry = 'Floppy Disks'", engine)

Visualize FinancialForce Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the FinancialForce Python Connector to start building Python apps and scripts with connectivity to FinancialForce 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("financialforce:///?User=myUser&Password=myPassword&Security Token=myToken&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT BillingState, Name FROM Account WHERE Industry = 'Floppy Disks'", engine)

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