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with Nasdaq Data Link (Time Series and Table data) and Python?
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Popular Nasdaq Data Link (Time Series and Table data) and Python Actions#
Exports an entire table or a filtered subset as a zipped CSV file. Returns a download link for the data. Premium subscribers can use this feature up to 60 times per hour. See the documentation
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Retrieves data from a specific Nasdaq Data Link table with automatic pagination. Supports filtering by columns and rows. See the documentation
Retrieves metadata for a specific Nasdaq Data Link table, including column names, types, filterable columns, and primary keys. See the documentation
Overview of Nasdaq Data Link (Time Series and Table data)#
The Nasdaq Data Link API provides access to financial, economic, and alternative data that powers investment decisions, research, and more. Within Pipedream, you can use this API to automate workflows involving time series and table data. This might include fetching stock prices, economic indicators, or data for quantitative analysis. By creating workflows that trigger on schedules or events, you can efficiently process and act upon this data. Combine it with other apps to gain insights, notify stakeholders, or integrate with your databases.
Connect Nasdaq Data Link (Time Series and Table data)#
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
nasdaq_data_link_time_series_and_table_data_: {
type: "app",
app: "nasdaq_data_link_time_series_and_table_data_",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://data.nasdaq.com/api/v3/datatables/ETFG/FUND.json`,
params: {
api_key: `${this.nasdaq_data_link_time_series_and_table_data_.$auth.api_key}`,
},
})
},
})
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}}Trusted by 1,000,000+ developers from startups to Fortune 500 companies