NiftyImages

Allowing marketers to create dynamic and personalized content for your email campaigns.

Integrate the NiftyImages API with the Python API

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

Add Data Store Record with the NiftyImages API

Create or update a Data Store Record See docs here

 
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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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Update Timer Target Date with the NiftyImages API

Create or update a Data Store Record See docs here

 
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Overview of NiftyImages

The NiftyImages API allows for the dynamic creation and customization of images for email marketing campaigns. By automating image personalization, users can create unique, engaging visuals that resonate with each recipient. It's possible to insert text into images, generate countdown timers, and manipulate image URLs on the fly, enhancing the visual appeal and relevance of marketing messages.

Connect NiftyImages

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import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    niftyimages: {
      type: "app",
      app: "niftyimages",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.niftyimages.com/v1/Widgets`,
      headers: {
        "ApiKey": `${this.niftyimages.$auth.api_key}`,
      },
    })
  },
})

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}}