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

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How to use SQLAlchemy ORM to access Gmail Data in Python


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

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

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

Connecting to Gmail Data

Connecting to Gmail 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.

There are two ways to authenticate to Gmail. Before selecting one, first ensure that you have enabled IMAP access in your Gmail account settings. See the "Connecting to Gmail" section under "Getting Started" in the installed documentation for a guide.

The User and Password properties, under the Authentication section, can be set to valid Gmail user credentials.

Alternatively, instead of providing the Password, you can use the OAuth authentication standard. To access Google APIs on behalf on individual users, you can use the embedded credentials or you can register your own OAuth app.

OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, you will need to register an application to obtain the OAuth JWT values.

In addition to the OAuth values, you will need to provide the User. See the "Getting Started" chapter in the help documentation for a guide to using OAuth.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Gmail 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("gmail:///?User=username&Password=password")

Declare a Mapping Class for Gmail 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 Inbox 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 Inbox(base): __tablename__ = "Inbox" Subject = Column(String,primary_key=True) Size = Column(String) ...

Query Gmail 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("gmail:///?User=username&Password=password") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Inbox).filter_by(From="test@test.com"): print("Subject: ", instance.Subject) print("Size: ", instance.Size) 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

Inbox_table = Inbox.metadata.tables["Inbox"] for instance in session.execute(Inbox_table.select().where(Inbox_table.c.From == "test@test.com")): print("Subject: ", instance.Subject) print("Size: ", instance.Size) print("---------")

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

Insert Gmail Data

To insert Gmail 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 Gmail.

new_rec = Inbox(Subject="placeholder", From="test@test.com") session.add(new_rec) session.commit()

Update Gmail Data

To update Gmail 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 Gmail.

updated_rec = session.query(Inbox).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.From = "test@test.com" session.commit()

Delete Gmail Data

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