Use SQLAlchemy ORMs to Access Paylocity Data in Python

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Paylocity Python Connector

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



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

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

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

Connecting to Paylocity Data

Connecting to Paylocity 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.

Set the following to establish a connection to Paylocity:

  • RSAPublicKey: Set this to the RSA Key associated with your Paylocity, if the RSA Encryption is enabled in the Paylocity account.

    This property is required for executing Insert and Update statements, and it is not required if the feature is disabled.

  • UseSandbox: Set to true if you are using sandbox account.
  • CustomFieldsCategory: Set this to the Customfields category. This is required when IncludeCustomFields is set to true. The default value for this property is PayrollAndHR.
  • Key: The AES symmetric key(base 64 encoded) encrypted with the Paylocity Public Key. It is the key used to encrypt the content.

    Paylocity will decrypt the AES key using RSA decryption.
    It is an optional property if the IV value not provided, The driver will generate a key internally.

  • IV: The AES IV (base 64 encoded) used when encrypting the content. It is an optional property if the Key value not provided, The driver will generate an IV internally.

Connect Using OAuth Authentication

You must use OAuth to authenticate with Paylocity. OAuth requires the authenticating user to interact with Paylocity using the browser. For more information, refer to the OAuth section in the Help documentation.

The Pay Entry API

The Pay Entry API is completely separate from the rest of the Paylocity API. It uses a separate Client ID and Secret, and must be explicitly requested from Paylocity for access to be granted for an account. The Pay Entry API allows you to automatically submit payroll information for individual employees, and little else. Due to the extremely limited nature of what is offered by the Pay Entry API, we have elected not to give it a separate schema, but it may be enabled via the UsePayEntryAPI connection property.

Please be aware that when setting UsePayEntryAPI to true, you may only use the CreatePayEntryImportBatch & MergePayEntryImportBatchgtable stored procedures, the InputTimeEntry table, and the OAuth stored procedures. Attempts to use other features of the product will result in an error. You must also store your OAuthAccessToken separately, which often means setting a different OAuthSettingsLocation when using this connection property.

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

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

engine = create_engine("paylocity:///?OAuthClientID=YourClientId&OAuthClientSecret=YourClientSecret&RSAPublicKey=YourRSAPubKey&Key=YourKey&IV=YourIV&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Paylocity 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 Employee 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 Employee(base):
	__tablename__ = "Employee"
	FirstName = Column(String,primary_key=True)
	LastName = Column(String)
	...

Query Paylocity 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("paylocity:///?OAuthClientID=YourClientId&OAuthClientSecret=YourClientSecret&RSAPublicKey=YourRSAPubKey&Key=YourKey&IV=YourIV&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Employee).filter_by(EmployeeId="1234"):
	print("FirstName: ", instance.FirstName)
	print("LastName: ", instance.LastName)
	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

Employee_table = Employee.metadata.tables["Employee"]
for instance in session.execute(Employee_table.select().where(Employee_table.c.EmployeeId == "1234")):
	print("FirstName: ", instance.FirstName)
	print("LastName: ", instance.LastName)
	print("---------")

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

Insert Paylocity Data

To insert Paylocity 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 Paylocity.

new_rec = Employee(FirstName="placeholder", EmployeeId="1234")
session.add(new_rec)
session.commit()

Update Paylocity Data

To update Paylocity 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 Paylocity.

updated_rec = session.query(Employee).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.EmployeeId = "1234"
session.commit()

Delete Paylocity Data

To delete Paylocity 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(Employee).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 Paylocity Python Connector to start building Python apps and scripts with connectivity to Paylocity data. Reach out to our Support Team if you have any questions.