Integrate the Faraday API with the Python API

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

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Popular Faraday and Python Actions#

Predict with the Faraday API

Returns a prediction about a single person. 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 Faraday#

The Faraday API empowers users to harness rich consumer data and predictive analytics for personalized marketing and strategic insights. On Pipedream, you can tap into this potential by creating workflows that trigger based on various events or schedules, process data with Faraday's capabilities, and then take actions – all without managing server infrastructure. Streamline customer interactions, forecast trends, and enrich contact lists by integrating other services for a seamless data-driven ecosystem.

Connect Faraday#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    faraday: {
      type: "app",
      app: "faraday",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.faraday.ai/v1/accounts/current`,
      headers: {
        Authorization: `Bearer ${this.faraday.$auth.api_key}`,
        "Accept": `*/*`,
      },
    })
  },
})

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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