Integrate the Token Metrics API with the Python API

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

Popular Token Metrics and Python Actions#

Get Market Metrics with the Token Metrics API

Gets the market analytics from Token Metrics. 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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Get Tokens with the Token Metrics API

Gets the list of coins and their associated token_id supported by Token Metrics. See the documentation

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Overview of Token Metrics#

The Token Metrics API offers access to a trove of cryptocurrency data, including analytics, rankings, and predictions that leverage artificial intelligence and expert insights. With this API, you can automate investment strategies, integrate up-to-date crypto data into your applications, and stay informed with the latest market trends. When used on Pipedream, it allows you to build robust, serverless workflows that can react to various triggers and integrate with numerous services for a seamless data handling experience.

Connect Token Metrics#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    token_metrics: {
      type: "app",
      app: "token_metrics",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.tokenmetrics.com/v2/tokens`,
      headers: {
        "accept": `application/json`,
        "api_key": `${this.token_metrics.$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}}