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Python

Kintone Python Connector

Read, write, and update Kintone with Python

Easily connect Python-based data access, visualization, ORM, ETL, AI/ML, and custom apps with Kintone!

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CData Drivers now work with AI Coding tools

Python Connector Libraries for Kintone Data Connectivity. Integrate Kintone with popular Python tools like Pandas, SQLAlchemy, Dash & petl. Easy-to-use Python Database API (DB-API) Modules connect Kintone data with Python and any Python-based applications.

Features

Powerful metadata querying enables SQL-like access to non-database sources
Push down query optimization pushes SQL operations down to the server whenever possible, increasing performance
Client-side query execution engine, supports SQL-92 operations that are not available server-side
Connect to live Kintone data, for real-time data access with the Kintone ODBC Driver
Full support for data aggregation and complex JOINs in SQL queries
Secure connectivity through modern cryptography, including TLS 1.2, SHA-256, ECC, etc.
Seamless integration with leading BI, reporting, and ETL tools and with custom applications via the Kintone Connector.

Specifications

Python Database API (DB-API) Modules for Kintone application data with bi-directional access.
Write SQL, get Kintone data. Access Kintone through standard Python Database Connectivity.
Integration with popular Python tools like Pandas, SQLAlchemy, Dash & petl.
Full Unicode support for data, parameter, & metadata.

CData Python connectors in action!

Watch the video overview for a first hand-look at the powerful data integration capabilities included in the CData Python Connectors.

Watch the Python Connector Video Overview

Python connectivity with Kintone

Full-featured and consistent SQL access to any supported data source through Python


Connecting to Kintone with Python

CData Python Connectors leverage the Database API (DB-API) interface to make it easy to work with Kintone from a wide range of standard Python data tools. Connecting to and working with your data in Python follows a basic pattern, regardless of data source:

  • Configure the connection properties to Kintone
  • Query Kintone to retrieve or update data
  • Connect your Kintone data with Python data tools.


Connecting to Kintone in Python

To connect to your data from Python, import the extension and create a connection:

Once you import the extension, you can work with all of your enterprise data using the python modules and toolkits that you already know and love, quickly building apps that help you drive business.

import cdata.kintone as mod
conn = mod.connect("[email protected]; Password=password;")

#Create cursor and iterate over results
cur = conn.cursor()
cur.execute("SELECT * FROM Apps")
	
rs = cur.fetchall()
	
for row in rs:
print(row)
		
engine = create_engine("kintone///Password=password&User=user")

df = pandas.read_sql("SELECT * FROM Apps", engine)

df.plot()
plt.show()

Visualize Kintone Data with pandas

The data-centric interfaces of the Kintone Python Connector make it easy to integrate with popular tools like pandas and SQLAlchemy to visualize data in real-time.

More than read-only: full update/CRUD support

Kintone Connector goes beyond read-only functionality to deliver full support for Create, Read Update, and Delete operations (CRUD). Your end-users can interact with the data presented by the Kintone Connector as easily as interacting with a database table.

AI-assisted development with CData CLI

Build Kintone integrations faster with AI that understands your schema

Schema-aware AI

CData CLI gives AI coding tools access to your Kintone schema. No more guessing table names or column types—AI sees the same metadata in your Python Connectors.

Your AI Knows SQL

How to find table names, column names, and how to generate SQL syntax are things that AI knows well from millions of training data. No need for customization, no hallucinations. Your AI acts like a domain specialist to Kintone.

More Accurate, More Token-Efficient

With CData CLI's queryable schema detection and highly efficient queries with filters, aggregation, joins with correct pushdown, your AI will achieve more accuracy with less token usage.

Supported AI Coding Tools
Download CData CLI