Integrate the ZenRows API with the Python API

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

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Popular ZenRows and Python Actions#

Get API Usage with the ZenRows API

Get Zenrows API usage. 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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Scrape URL Autoparse with the ZenRows API

Scrape HTML of the URL. See the documentation

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Scrape URL CSS Selectors with the ZenRows API

Scrape HTML of the URL with CSS Selectors. See the documentation

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Overview of ZenRows#

ZenRows API specializes in web scraping and handles issues like CAPTCHAs, JavaScript rendering, and rotating proxies to ensure successful data extraction. In Pipedream, you can pair the ZenRows API with numerous other services to create automated workflows that respond to events, process and analyze scraped data, or even trigger actions based on the data collected. Whether you need to monitor changes on web pages, aggregate content for analysis, or feed scraped data into other applications, ZenRows' integration on Pipedream simplifies these tasks.

Connect ZenRows#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    zenrows: {
      type: "app",
      app: "zenrows",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.zenrows.com/v1/`,
      params: {
        apikey: `${this.zenrows.$auth.api_key}`,
        url: `https://httpbin.io/anything`,
      },
    })
  },
})

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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