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Extract, Transform, and Load OFX Data in Python

The CData Python Connector for OFX enables you to create ETL applications and pipelines for OFX data in Python with petl.

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 and the petl framework, you can build OFX-connected applications and pipelines for extracting, transforming, and loading OFX data. This article shows how to connect to OFX with the CData Python Connector and use petl and pandas to extract, transform, and load OFX data.

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.

After installing the CData OFX Connector, follow the procedure below to install the other required modules and start accessing OFX through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for OFX Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.ofx as mod

You can now connect with a connection string. Use the connect function for the CData OFX Connector to create a connection for working with OFX data.

cnxn = mod.connect("OFXUser=myUser;OFXPassword=myPassword;FIID=myFIID;")

Create a SQL Statement to Query OFX

Use SQL to create a statement for querying OFX. In this article, we read data from the InvBalances entity.

sql = "SELECT Id, Amount FROM InvBalances WHERE ServiceType = 'CREDITCARD'"

Extract, Transform, and Load the OFX Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the OFX data. In this example, we extract OFX data, sort the data by the Amount column, and load the data into a CSV file.

Loading OFX Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Amount')

etl.tocsv(table2,'invbalances_data.csv')

With the CData Python Connector for OFX, you can work with OFX data just like you would with any database, including direct access to data in ETL packages like petl.

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 petl as etl
import pandas as pd
import cdata.ofx as mod

cnxn = mod.connect("OFXUser=myUser;OFXPassword=myPassword;FIID=myFIID;")

sql = "SELECT Id, Amount FROM InvBalances WHERE ServiceType = 'CREDITCARD'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Amount')

etl.tocsv(table2,'invbalances_data.csv')