Extract, Transform, and Load Wave Financial Data in Python

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Wave Financial Python Connector

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



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

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

Connecting to Wave Financial Data

Connecting to Wave Financial 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.

Connect using the API Token

You can connect to Wave Financial by specifying the APIToken You can obtain an API Token using the following steps:

  1. Log in to your Wave account and navigate to "Manage Applications" in the left pane.
  2. Select the application that you would like to create a token for. You may need to create an application first.
  3. Click the "Create token" button to generate an APIToken.

Connect using OAuth

If you wish, you can connect using the embedded OAuth credentials. See the Help documentation for more information.

After installing the CData Wave Financial Connector, follow the procedure below to install the other required modules and start accessing Wave Financial 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 Wave Financial 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.wavefinancial as mod

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Wave Financial

Use SQL to create a statement for querying Wave Financial. In this article, we read data from the Invoices entity.

sql = "SELECT Id, DueDate FROM Invoices WHERE Status = 'SENT'"

Extract, Transform, and Load the Wave Financial Data

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

Loading Wave Financial Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

In the following example, we add new rows to the Invoices table.

Adding New Rows to Wave Financial

table1 = [ ['Id','DueDate'], ['NewId1','NewDueDate1'], ['NewId2','NewDueDate2'], ['NewId3','NewDueDate3'] ]

etl.appenddb(table1, cnxn, 'Invoices')

With the CData Python Connector for Wave Financial, you can work with Wave Financial 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 Wave Financial Python Connector to start building Python apps and scripts with connectivity to Wave Financial 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.wavefinancial as mod

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Id, DueDate FROM Invoices WHERE Status = 'SENT'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Id','DueDate'], ['NewId1','NewDueDate1'], ['NewId2','NewDueDate2'], ['NewId3','NewDueDate3'] ]

etl.appenddb(table3, cnxn, 'Invoices')