Ready to get started?

Learn more about the CData Python Connector for Quandl or download a free trial:

Download Now

Use SQLAlchemy ORMs to Access Quandl Data in Python

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

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

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

Connecting to Quandl Data

Connecting to Quandl 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.

Quandl uses an API key for authentication. See the help documentation for a guide to obtaining the APIKey property.

Additionally, set the DatabaseCode connection property to the code identifying the Database whose Datasets you want to query with SQL. You can search the available Databases by querying the Databases view.

Follow the procedure below to install SQLAlchemy and start accessing Quandl through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit:

pip install sqlalchemy

Be sure to import the module with the following:

import sqlalchemy

Model Quandl Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Quandl data.

engine = create_engine("quandl///?APIKey=abc123&DatabaseCode=WIKI")

Declare a Mapping Class for Quandl 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 AAPL 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 AAPL(base):
	__tablename__ = "AAPL"
	Date = Column(String,primary_key=True)
	Volume = Column(String)

Query Quandl 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("quandl///?APIKey=abc123&DatabaseCode=WIKI")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(AAPL).filter_by(Collapse="Daily"):
	print("Date: ", instance.Date)
	print("Volume: ", instance.Volume)

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

AAPL_table = AAPL.metadata.tables["AAPL"]
for instance in session.execute( == "Daily")):
	print("Date: ", instance.Date)
	print("Volume: ", instance.Volume)

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

Insert Quandl Data

To insert Quandl 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 Quandl.

new_rec = AAPL(Date="placeholder", Collapse="Daily")

Update Quandl Data

To update Quandl 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 Quandl.

updated_rec = session.query(AAPL).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Collapse = "Daily"

Delete Quandl Data

To delete Quandl 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 recoreds (rows).

deleted_rec = session.query(AAPL).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()

Free Trial & More Information

Download a free, 30-day trial of the Quandl Python Connector to start building Python apps and scripts with connectivity to Quandl data. Reach out to our Support Team if you have any questions.