ADP

Easy & Accurate Payroll. Managing your people has never been easier with ADP's all-in-one, cloud-based platform for payroll, HR, time, talent and benefits.

Integrate the ADP API with the Python API

Setup the ADP API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate ADP and Python remarkably fast. Free for developers.

Run Python Code with the Python API

Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.

 
Try it

Overview of ADP

The ADP API provides access to a breadth of payroll and human capital management services. With Pipedream, you can automate workflows that bridge the gap between ADP and other apps, streamlining your HR processes. By leveraging Pipedream's serverless platform, you can orchestrate data flows, synchronize employee information, manage payroll events, and react to changes in ADP data in real-time without writing extensive code.

Connect ADP

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import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    adp: {
      type: "app",
      app: "adp",
    }
  },
  async run({steps, $}) {
    const httpsAgent = new https.Agent({
      cert: this.adp.$auth.crt_file,
      key: this.adp.$auth.key_file,
    })

    return await axios($, {
      url: `https://api.adp.com/hr/v2/workers`,
      headers: {
        Authorization: `Bearer ${this.adp.$auth.oauth_access_token}`,
      },
      httpsAgent: httpsAgent
    })
  },
})

Overview of Python

Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.

This includes installing PyPI packages, within your code without having to manage a requirements.txt file or running pip.

Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:

Connect Python

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def handler(pd: "pipedream"):
  # Reference data from previous steps
  print(pd.steps["trigger"]["context"]["id"])
  # Return data for use in future steps
  return {"foo": {"test":True}}