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DeTrack

Detrack is a real-time vehicle tracking & electronic proof of delivery solution that works anywhere in the world.

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DeTrack

Python

Integrate the DeTrack API with the Python API

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

Popular DeTrack and Python Actions#

Create Job with the DeTrack API

Create a job 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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Overview of DeTrack#

The DeTrack API offers the functionality to track deliveries and vehicles, manage delivery-related data, and automate communication between logistics stakeholders. Leveraging the DeTrack API on Pipedream, you can create a seamless flow of delivery information, reduce manual work, and keep all parties up-to-date with real-time notifications. This API’s capabilities when integrated into workflows can streamline inventory management, enhance customer service, and optimize delivery operations.

Connect DeTrack#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    detrack: {
      type: "app",
      app: "detrack",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://app.detrack.com/api/v2/dn/jobs`,
      headers: {
        "X-API-KEY": `${this.detrack.$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#

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}}