SeaTable
With SeaTable, teams can easily organize all their tasks, assets, projects and ideas. It looks like a spreadsheet but structures any type of information, in the cloud or on your own server.
Integrate the SeaTable API with the Python API
Setup the SeaTable API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate SeaTable and Python remarkably fast. Free for developers.
Popular SeaTable and Python Actions#
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Deletes a specific row from a specified table. See the documentation
Overview of SeaTable#
The SeaTable API allows you to interact programmatically with SeaTable databases. With it, you can create, update, and delete records, rows, and columns, manipulate tables, and manage users. In Pipedream, you can harness this API to automate data flows between SeaTable and other apps, trigger workflows based on changes in your SeaTable bases, and manipulate data without manual intervention. Think of it as connecting your spreadsheets' data to the wider web.
Connect SeaTable#
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
seatable: {
type: "app",
app: "seatable",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://cloud.seatable.io/api2/account/info/`,
headers: {
Authorization: `Bearer ${this.seatable.$auth.account_token}`,
},
})
},
})
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}}