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Use SQLAlchemy ORMs to Access CSV Data in Python

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

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

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

Connecting to CSV Data

Connecting to CSV 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.

The DataSource property must be set to a valid local folder name.

Also, specify the IncludeFiles property to work with text files having extensions that differ from .csv, .tab, or .txt. Specify multiple file extensions in a comma-separated list. You can also set Extended Properties compatible with the Microsoft Jet OLE DB 4.0 driver. Alternatively, you can provide the format of text files in a Schema.ini file.

Set UseRowNumbers to true if you are deleting or updating in CSV. This will create a new column with the name RowNumber which will be used as key for that table.

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

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

engine = create_engine("csv///?DataSource=MyCSVFilesFolder")

Declare a Mapping Class for CSV 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 Customer 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 Customer(base):
	__tablename__ = "Customer"
	City = Column(String,primary_key=True)
	TotalDue = Column(String)
	...

Query CSV 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("csv///?DataSource=MyCSVFilesFolder")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Customer).filter_by(FirstName="Bob"):
	print("City: ", instance.City)
	print("TotalDue: ", instance.TotalDue)
	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

Customer_table = Customer.metadata.tables["Customer"]
for instance in session.execute(Customer_table.select().where(Customer_table.c.FirstName == "Bob")):
	print("City: ", instance.City)
	print("TotalDue: ", instance.TotalDue)
	print("---------")

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

Free Trial & More Information

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