Mode
Answer any question, fast. Mode brings SQL, Python, Rstats and custom visualizations together in one analytics platform.
Integrate the Mode API with the Python API
Setup the Mode API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate Mode and Python remarkably fast. Free for developers.
Popular Mode and Python Actions#
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Overview of Mode#
The Mode API provides programmatic access to Mode's analytics platform, allowing you to automate interactions with your Mode workspace. Using Mode with Pipedream, you can create workflows that interact with reports, queries, and spaces, or even manage members and permissions. This can include tasks like triggering a report run, fetching query results, or syncing users from other systems into Mode. By leveraging Pipedream's serverless platform, you can build robust, event-driven automations that integrate Mode with hundreds of other apps without the need for dedicated infrastructure.
Connect Mode#
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
mode: {
type: "app",
app: "mode",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://app.mode.com/api/account`,
headers: {
"Accept": `application/json`,
"Content-Type": `application/json`,
},
auth: {
username: `${this.mode.$auth.token}`,
password: `${this.mode.$auth.password}`,
},
})
},
})
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}}