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The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData API Driver for Python and the SQLAlchemy toolkit, you can build Drip-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Drip data to query Drip data.
With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Drip data in Python. When you issue complex SQL queries from Drip, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Drip and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to Drip 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.
Start by setting the Profile connection property to the location of the Drip Profile on disk (e.g. C:\profiles\Drip.apip). Next, set the ProfileSettings connection property to the connection string for Drip (see below).
To use Token Authentication, specify your APIKey within the ProfileSettings connection property. The APIKey should be set to your Drip personal API Token.
Follow the procedure below to install SQLAlchemy and start accessing Drip through Python objects.
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
You can now connect with a connection string. Use the create_engine function to create an Engine for working with Drip 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("api:///?Profile=C:\profiles\Drip.apip&ProfileSettings='APIKey=my_api_token'")
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 Broadcasts 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 Broadcasts(base): __tablename__ = "Broadcasts" Id = Column(String,primary_key=True) Name = Column(String) ...
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.
engine = create_engine("api:///?Profile=C:\profiles\Drip.apip&ProfileSettings='APIKey=my_api_token'")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Broadcasts).filter_by(Status="scheduled"):
print("Id: ", instance.Id)
print("Name: ", instance.Name)
print("---------")
Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.
Broadcasts_table = Broadcasts.metadata.tables["Broadcasts"]
for instance in session.execute(Broadcasts_table.select().where(Broadcasts_table.c.Status == "scheduled")):
print("Id: ", instance.Id)
print("Name: ", instance.Name)
print("---------")
For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.
Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Drip data. Reach out to our Support Team if you have any questions.
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