Search hundreds of datasets from the City and County of San Francisco
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
The San Francisco Open Data - DataSF API unlocks a wealth of government data spanning multiple domains such as transportation, housing, and public health. It provides developers with access to rich datasets, which can be integrated into applications to derive insights, inform decision-making, and power data-driven solutions. Pipedream's serverless platform amplifies this potential by enabling users to create automated workflows that leverage this data in concert with other apps and services.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
san_francisco_open_data_datasf: {
type: "app",
app: "san_francisco_open_data_datasf",
}
},
async run({steps, $}) {
// Below, we fetch a range of COVID-19 case data from DataSF. You can
// run this to see how the results are displayed on Pipedream, or modify it
// in any way to fetch data from another dataset or modify the Socrata
// query. See the docs below for Socrata docs and examples.
// COVID-19 Cases Summarized by Date, Transmission and Case Disposition
// https://dev.socrata.com/foundry/data.sfgov.org/tvq9-ec9w
return await axios($, {
url: `https://data.sfgov.org/resource/tvq9-ec9w.json`,
headers: {
"X-App-Token": `${this.san_francisco_open_data_datasf.$auth.app_token}`,
},
params: {
"$where": `date between '2020-05-18T00:00:00' and '2020-05-20T00:00:00'`,
},
})
},
})
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:
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}}