Ready to get started?

Learn more about the CData Python Connector for Kintone or download a free trial:

Download Now

Extract, Transform, and Load Kintone Data in Python

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

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

Connecting to Kintone Data

Connecting to Kintone 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.

In addition to the authentication values, set the following parameters to connect to and retrieve data from Kintone:

  • Url: The URL of your account.
  • GuestSpaceId: Optional. Set this when using a guest space.

Authennticating with Kintone

Kintone supports the following authentication methods.

Using Password Authentication

You must set the following to authenticate:

  • User: The username of your account.
  • Password: The password of your account.

Using Basic Authentication

If the basic authentication security feature is set on the domain, supply the additional login credentials with BasicAuthUser and BasicAuthPassword. Basic authentication requires these credentials in addition to User and Password.

Using Client SSL

Instead of basic authentication, you can specify a client certificate to authenticate. Set SSLClientCert, SSLClientCertType, SSLClientCertSubject, and SSLClientCertPassword. Additionally, set User and Password to your login credentials.

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

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

cnxn = mod.connect("User=myuseraccount;Password=mypassword;Url=http://subdomain.domain.com;GuestSpaceId=myspaceid")

Create a SQL Statement to Query Kintone

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

sql = "SELECT CreatorName, Text FROM Comments WHERE AppId = '1354841'"

Extract, Transform, and Load the Kintone Data

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

Loading Kintone Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Kintone

table1 = [ ['CreatorName','Text'], ['NewCreatorName1','NewText1'], ['NewCreatorName2','NewText2'], ['NewCreatorName3','NewText3'] ]

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

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

cnxn = mod.connect("User=myuseraccount;Password=mypassword;Url=http://subdomain.domain.com;GuestSpaceId=myspaceid")

sql = "SELECT CreatorName, Text FROM Comments WHERE AppId = '1354841'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['CreatorName','Text'], ['NewCreatorName1','NewText1'], ['NewCreatorName2','NewText2'], ['NewCreatorName3','NewText3'] ]

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