Integrate the SharpSpring API with the Python API

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

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Popular SharpSpring and Python Actions#

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 SharpSpring#

The SharpSpring API offers a gateway to an extensive marketing platform, enabling you to automate your marketing workflows, manage customer relationships, and glean insights from comprehensive analytics. With Pipedream, you can harness this power to create seamless integrations that trigger actions in SharpSpring based on external events or data, sync leads and contacts across platforms, and personalize marketing efforts based on user behavior and preferences.

Connect SharpSpring#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    sharpspring: {
      type: "app",
      app: "sharpspring",
    }
  },
  async run({steps, $}) {
    const data = {
      "method": `getLead`,
      "params": `{
        "id":"<a lead id>"
      }`,
      "id": `<your request ID>`,
    }
    return await axios($, {
      url: `https://api.sharpspring.com/pubapi/v1/`,
      params: {
        accountID: `${this.sharpspring.$auth.account_id}`,
        secretKey: `${this.sharpspring.$auth.secret_key}`,
      },
      data,
    })
  },
})

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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