Integrate the MaxMind GeoIP2 API with the Python API

Setup the MaxMind GeoIP2 API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate MaxMind GeoIP2 and Python remarkably fast. Free for developers.

Popular MaxMind GeoIP2 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.

Try it

Overview of MaxMind GeoIP2#

The MaxMind GeoIP2 API enables you to identify the geographical location of your users based on their IP addresses. It offers data such as country, city, postal code, latitude and longitude, and more. On Pipedream, you can leverage this API to create powerful workflows that respond to IP-based events with geo-specific outcomes. Whether for security, personalization, or data analytics, integrating GeoIP2 within Pipedream workflows allows you to automate actions based on user locations.

Connect MaxMind GeoIP2#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    maxmind_geoip2: {
      type: "app",
      app: "maxmind_geoip2",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://geoip.maxmind.com/geoip/v2.1/country/me`,
      headers: {
        "Accept": `application/json`,
      },
      auth: {
        username: `${this.maxmind_geoip2.$auth.account_id}`,
        password: `${this.maxmind_geoip2.$auth.license_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}}