Integrate the Email Verifier Api API with the Python API

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

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Popular Email Verifier Api and Python Actions#

Verify Email with the Email Verifier Api API

Verify an email address with Email Verifier API. See the documentation

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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.

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Overview of Email Verifier Api#

The Email Verifier API provides a means to validate email addresses in real-time, checking their existence, validity, and quality. By integrating this API with Pipedream, you can automate workflows that improve your email marketing efforts, maintain a clean email list, and enhance user verification processes. Using Pipedream's serverless platform, you can connect the Email Verifier API with numerous other services to create custom, event-driven workflows.

Connect Email Verifier Api#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    email_verifier_api: {
      type: "app",
      app: "email_verifier_api",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://emailverifierapi.com/v2/`,
      params: {
        apiKey: `${this.email_verifier_api.$auth.api_key}`,
        email: `sergio@pipekit.pro`,
      },
    })
  },
})

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#

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}}

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