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

How to use SQLAlchemy ORM to access Airtable Data in Python

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

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

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

Connecting to Airtable Data

Connecting to Airtable 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.

APIKey, BaseId and TableNames parameters are required to connect to Airtable. ViewNames is an optional parameter where views of the tables may be specified.

  • APIKey : API Key of your account. To obtain this value, after logging in go to Account. In API section click Generate API key.
  • BaseId : Id of your base. To obtain this value, it is in the same section as the APIKey. Click on Airtable API, or navigate to and select a base. In the introduction section you can find "The ID of this base is appxxN2ftedc0nEG7."
  • TableNames : A comma separated list of table names for the selected base. These are the same names of tables as found in the UI.
  • ViewNames : A comma separated list of views in the format of (table.view) names. These are the same names of the views as found in the UI.

Follow the procedure below to install SQLAlchemy and start accessing Airtable 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 Airtable Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Airtable 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("airtable:///?APIKey=keymz3adb53RqsU&BaseId=appxxN2fe34r3rjdG7&TableNames=Table1,...&ViewNames=Table1.View1,...")

Declare a Mapping Class for Airtable 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 SampleTable_1 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 SampleTable_1(base): __tablename__ = "SampleTable_1" Id = Column(String,primary_key=True) Column1 = Column(String) ...

Query Airtable 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("airtable:///?APIKey=keymz3adb53RqsU&BaseId=appxxN2fe34r3rjdG7&TableNames=Table1,...&ViewNames=Table1.View1,...") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(SampleTable_1).filter_by(Column2="SomeValue"): print("Id: ", instance.Id) print("Column1: ", instance.Column1) 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

SampleTable_1_table = SampleTable_1.metadata.tables["SampleTable_1"] for instance in session.execute( == "SomeValue")): print("Id: ", instance.Id) print("Column1: ", instance.Column1) print("---------")

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

Insert Airtable Data

To insert Airtable 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 Airtable.

new_rec = SampleTable_1(Id="placeholder", Column2="SomeValue") session.add(new_rec) session.commit()

Update Airtable Data

To update Airtable 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 Airtable.

updated_rec = session.query(SampleTable_1).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Column2 = "SomeValue" session.commit()

Delete Airtable Data

To delete Airtable 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(SampleTable_1).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 Airtable to start building Python apps and scripts with connectivity to Airtable data. Reach out to our Support Team if you have any questions.