RAWG Video Games Database
The largest video game database and video game discovery service.
Integrate the RAWG Video Games Database API with the Python API
Setup the RAWG Video Games Database API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate RAWG Video Games Database and Python remarkably fast. Free for developers.
Popular RAWG Video Games Database 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 RAWG Video Games Database#
The RAWG Video Games Database API is a treasure trove for gaming enthusiasts and developers, offering access to a massive collection of video game data. With it, you can pull information such as game titles, ratings, platforms, and release dates. This data can power up apps or websites with rich content or be used to analyze gaming trends. Through Pipedream, you can automate data collection, trigger events based on new or updated game releases, and integrate with other services for notifications or data enrichment.
Connect RAWG Video Games Database#
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
rawg_video_games_database: {
type: "app",
app: "rawg_video_games_database",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.rawg.io/api/platforms`,
params: {
key: `${this.rawg_video_games_database.$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}}