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Extract, Transform, and Load Odoo Data in Python

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

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

Connecting to Odoo Data

Connecting to Odoo 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.

To connect, set the Url to a valid Odoo site, User and Password to the connection details of the user you are connecting with, and Database to the Odoo database.

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

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

cnxn = mod.connect("User=MyUser;Password=MyPassword;URL=http://MyOdooSite/;Database=MyDatabase;")

Create a SQL Statement to Query Odoo

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

sql = "SELECT name, email FROM res_users WHERE id = '1'"

Extract, Transform, and Load the Odoo Data

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

Loading Odoo Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Odoo

table1 = [ ['name','email'], ['Newname1','Newemail1'], ['Newname2','Newemail2'], ['Newname3','Newemail3'] ]

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

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

cnxn = mod.connect("User=MyUser;Password=MyPassword;URL=http://MyOdooSite/;Database=MyDatabase;")

sql = "SELECT name, email FROM res_users WHERE id = '1'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['name','email'], ['Newname1','Newemail1'], ['Newname2','Newemail2'], ['Newname3','Newemail3'] ]

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