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Use pandas to Visualize OFX Data in Python

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

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

Connecting to OFX Data

Connecting to OFX 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 OFXUser and OFXPassword properties, under the Authentication section, must be set to valid OFX user credentials. In addition to this, you will need to configure FIURL, FIOrganizationName, and FIID, which will be specific for the financial institution. You will also need to provide application-specific settings, including OFXVersion, ApplicationVersion, and ApplicationId.

To connect to some services, you will need to provide additional account information such as AccountId, AccountType, BankId, BrokerId, and CCNumber.

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

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

engine = create_engine("ofx:///?OFXUser=myUser&OFXPassword=myPassword&FIID=myFIID")

Execute SQL to OFX

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, Amount FROM InvBalances WHERE ServiceType = 'CREDITCARD'", engine)

Visualize OFX Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the OFX Python Connector to start building Python apps and scripts with connectivity to OFX 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("ofx:///?OFXUser=myUser&OFXPassword=myPassword&FIID=myFIID")
df = pandas.read_sql("SELECT Id, Amount FROM InvBalances WHERE ServiceType = 'CREDITCARD'", engine)

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