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

How to use SQLAlchemy ORM to access Kintone Data in Python

Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Kintone data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Kintone and the SQLAlchemy toolkit, you can build Kintone-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Kintone data to query, update, delete, and insert 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 CData Connector 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.

Authenticating 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.

Follow the procedure below to install SQLAlchemy and start accessing Kintone through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker

Model Kintone Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Kintone data.

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("kintone:///?User=myuseraccount&Password=mypassword&Url=")

Declare a Mapping Class for Kintone Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Comments table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base() class Comments(base): __tablename__ = "Comments" CreatorName = Column(String,primary_key=True) Text = Column(String) ...

Query Kintone Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("kintone:///?User=myuseraccount&Password=mypassword&Url=") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Comments).filter_by(AppId="1354841"): print("CreatorName: ", instance.CreatorName) print("Text: ", instance.Text) print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Comments_table = Comments.metadata.tables["Comments"] for instance in session.execute( == "1354841")): print("CreatorName: ", instance.CreatorName) print("Text: ", instance.Text) print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Insert Kintone Data

To insert Kintone data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to Kintone.

new_rec = Comments(CreatorName="placeholder", AppId="1354841") session.add(new_rec) session.commit()

Update Kintone Data

To update Kintone data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to Kintone.

updated_rec = session.query(Comments).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.AppId = "1354841" session.commit()

Delete Kintone Data

To delete Kintone data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).

deleted_rec = session.query(Comments).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() session.delete(deleted_rec) session.commit()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Kintone to start building Python apps and scripts with connectivity to Kintone data. Reach out to our Support Team if you have any questions.