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

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How to use SQLAlchemy ORM to access Google Calendar Data in Python


The CData Python Connector for Google Calendars enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Google Calendar data.

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

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

Connecting to Google Calendar Data

Connecting to Google Calendar 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.

You can connect to Google APIs on behalf of individual users or on behalf of a domain. Google uses the OAuth authentication standard. See the "Getting Started" section of the help documentation for a guide.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Google Calendar 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("googlecalendar:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Google Calendar 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 VacationCalendar 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 VacationCalendar(base): __tablename__ = "VacationCalendar" Summary = Column(String,primary_key=True) StartDateTime = Column(String) ...

Query Google Calendar 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("googlecalendar:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(VacationCalendar).filter_by(SearchTerms="beach trip"): print("Summary: ", instance.Summary) print("StartDateTime: ", instance.StartDateTime) 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

VacationCalendar_table = VacationCalendar.metadata.tables["VacationCalendar"] for instance in session.execute(VacationCalendar_table.select().where(VacationCalendar_table.c.SearchTerms == "beach trip")): print("Summary: ", instance.Summary) print("StartDateTime: ", instance.StartDateTime) print("---------")

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

Insert Google Calendar Data

To insert Google Calendar 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 Google Calendar.

new_rec = VacationCalendar(Summary="placeholder", SearchTerms="beach trip") session.add(new_rec) session.commit()

Update Google Calendar Data

To update Google Calendar 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 Google Calendar.

updated_rec = session.query(VacationCalendar).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.SearchTerms = "beach trip" session.commit()

Delete Google Calendar Data

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