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

Use SQLAlchemy ORMs to Access ADP Data in Python

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

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

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

Connecting to ADP Data

Connecting to ADP 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.

Connect to ADP by specifying the following properties:

  • SSLClientCert: Set this to the certificate provided during registration.
  • SSLClientCertPassword: Set this to the password of the certificate.
  • UseUAT: The connector makes requests to the production environment by default. If using a developer account, set UseUAT = true.
  • RowScanDepth: The maximum number of rows to scan for the custom fields columns available in the table. The default value will be set to 100. Setting a high value may decrease performance.

The connector uses OAuth to authenticate with ADP. OAuth requires the authenticating user to interact with ADP using the browser. For more information, refer to the OAuth section in the Help documentation.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with ADP 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("adp:///?OAuthClientId=YourClientId&OAuthClientSecret=YourClientSecret&SSLClientCert='c:\cert.pfx'&SSLClientCertPassword='admin@123'InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for ADP 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 Workers 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 Workers(base): __tablename__ = "Workers" AssociateOID = Column(String,primary_key=True) WorkerID = Column(String) ...

Query ADP 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("adp:///?OAuthClientId=YourClientId&OAuthClientSecret=YourClientSecret&SSLClientCert='c:\cert.pfx'&SSLClientCertPassword='admin@123'InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Workers).filter_by(AssociateOID="G3349PZGBADQY8H8"): print("AssociateOID: ", instance.AssociateOID) print("WorkerID: ", instance.WorkerID) 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

Workers_table = Workers.metadata.tables["Workers"] for instance in session.execute( == "G3349PZGBADQY8H8")): print("AssociateOID: ", instance.AssociateOID) print("WorkerID: ", instance.WorkerID) print("---------")

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

Insert ADP Data

To insert ADP 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 ADP.

new_rec = Workers(AssociateOID="placeholder", AssociateOID="G3349PZGBADQY8H8") session.add(new_rec) session.commit()

Update ADP Data

To update ADP 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 ADP.

updated_rec = session.query(Workers).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.AssociateOID = "G3349PZGBADQY8H8" session.commit()

Delete ADP Data

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