Extract, Transform, and Load Paylocity Data in Python

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Paylocity and the petl framework, you can build Paylocity-connected applications and pipelines for extracting, transforming, and loading Paylocity data. This article shows how to connect to Paylocity with the CData Python Connector and use petl and pandas to extract, transform, and load 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 driver 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.

After installing the CData Paylocity Connector, follow the procedure below to install the other required modules and start accessing Paylocity through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Paylocity Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.paylocity as mod

You can now connect with a connection string. Use the connect function for the CData Paylocity Connector to create a connection for working with Paylocity data.

cnxn = mod.connect("OAuthClientID=YourClientId;OAuthClientSecret=YourClientSecret;RSAPublicKey=YourRSAPubKey;Key=YourKey;IV=YourIV;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Paylocity

Use SQL to create a statement for querying Paylocity. In this article, we read data from the Employee entity.

sql = "SELECT FirstName, LastName FROM Employee WHERE EmployeeId = '1234'"

Extract, Transform, and Load the Paylocity Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Paylocity data. In this example, we extract Paylocity data, sort the data by the LastName column, and load the data into a CSV file.

Loading Paylocity Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'LastName')

etl.tocsv(table2,'employee_data.csv')

In the following example, we add new rows to the Employee table.

Adding New Rows to Paylocity

table1 = [ ['FirstName','LastName'], ['NewFirstName1','NewLastName1'], ['NewFirstName2','NewLastName2'], ['NewFirstName3','NewLastName3'] ]

etl.appenddb(table1, cnxn, 'Employee')

With the CData Python Connector for Paylocity, you can work with Paylocity data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

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

Full Source Code

import petl as etl
import pandas as pd
import cdata.paylocity as mod

cnxn = mod.connect("OAuthClientID=YourClientId;OAuthClientSecret=YourClientSecret;RSAPublicKey=YourRSAPubKey;Key=YourKey;IV=YourIV;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT FirstName, LastName FROM Employee WHERE EmployeeId = '1234'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'LastName')

etl.tocsv(table2,'employee_data.csv')

table3 = [ ['FirstName','LastName'], ['NewFirstName1','NewLastName1'], ['NewFirstName2','NewLastName2'], ['NewFirstName3','NewLastName3'] ]

etl.appenddb(table3, cnxn, 'Employee')