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

How to use SQLAlchemy ORM to access YouTube Analytics Data in Python

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

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

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

Connecting to YouTube Analytics Data

Connecting to YouTube Analytics 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.

YouTube Analytics uses the OAuth authentication standard. You can use the embedded CData OAuth credentials or you can register an application with Google to obtain your own.

In addition to the OAuth values, to access YouTube Analytics data set ChannelId to the Id of a YouTube channel. You can obtain the channel Id in the advanced account settings for your channel. If not specified, the channel of the currently authenticated user will be used.

If you want to generate content owner reports, specify the ContentOwnerId property. This is the Id of the copyright holder for content in YouTube's rights management system. The content owner is the person or organization that claims videos and sets their monetization policy.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with YouTube Analytics 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("youtubeanalytics:///?ContentOwnerId=MyContentOwnerId&ChannelId=MyChannelId&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for YouTube Analytics 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 Groups 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 Groups(base): __tablename__ = "Groups" Snippet_Title = Column(String,primary_key=True) ContentDetails_ItemCount = Column(String) ...

Query YouTube Analytics 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("youtubeanalytics:///?ContentOwnerId=MyContentOwnerId&ChannelId=MyChannelId&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Groups).filter_by(Mine="True"): print("Snippet_Title: ", instance.Snippet_Title) print("ContentDetails_ItemCount: ", instance.ContentDetails_ItemCount) 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

Groups_table = Groups.metadata.tables["Groups"] for instance in session.execute( == "True")): print("Snippet_Title: ", instance.Snippet_Title) print("ContentDetails_ItemCount: ", instance.ContentDetails_ItemCount) print("---------")

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

Insert YouTube Analytics Data

To insert YouTube Analytics 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 YouTube Analytics.

new_rec = Groups(Snippet_Title="placeholder", Mine="True") session.add(new_rec) session.commit()

Update YouTube Analytics Data

To update YouTube Analytics 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 YouTube Analytics.

updated_rec = session.query(Groups).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Mine = "True" session.commit()

Delete YouTube Analytics Data

To delete YouTube Analytics 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(Groups).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() session.delete(deleted_rec) session.commit()

Free Trial & More Information

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