Integrate the SheetDB API with the Python API

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

Popular SheetDB and Python Actions#

Create Rows with the SheetDB API

Create rows in a Google Sheet using the SheetDB API. 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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Delete Rows with the SheetDB API

Deletes the specified row(s) in a SheetDB sheet by matching a column name and value. See the documentation

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Get Column Names with the SheetDB API

Get column names of a Google Sheet using the SheetDB API. See the documentation

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Search Content with the SheetDB API

Search for content in a Google Sheet using the SheetDB API. See the documentation

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Overview of SheetDB#

SheetDB API turns your Google Sheets into a JSON API, enabling you to manage the content within your spreadsheet through RESTful endpoints. With Pipedream, you can harness this capability to build robust automations and workflows that interact with your spreadsheet data dynamically. Whether you're updating rows based on external triggers, syncing data to other platforms, or building a makeshift CRM, SheetDB paired with Pipedream's zero-management execution environment lets you deploy these solutions rapidly.

Connect SheetDB#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    sheetdb: {
      type: "app",
      app: "sheetdb",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://sheetdb.io/api/v1/${this.sheetdb.$auth.api_id}`,
    })
  },
})

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}}