Shopify is a user-friendly e-commerce platform that helps small businesses build an online store and sell online through one streamlined dashboard.
Emit new event each time a user abandons their cart.
Emit new event each time a new order is cancelled.
Emit new event for each new customer added to a store.
Emit new event for each new draft order submitted to a store.
Adds a product or products to a custom collection or collections. See the docs
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
By creating a custom app on Shopify, you will be able to configure the exact scopes that you require to build the workflows that you need.
The Shopify Developer App API unleashes a myriad of possibilities to automate and enhance online store operations. It provides programmatic access to Shopify functionalities, allowing users to manage products, customers, orders, and more. Leveraging the API within Pipedream, developers can create custom workflows that automate repetitive tasks, sync data across platforms, and respond dynamically to events in Shopify.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
shopify_developer_app: {
type: "app",
app: "shopify_developer_app",
}
},
async run({steps, $}) {
const data = {
"query": `{
shop {
id
name
email
}
}`,
}
return await axios($, {
method: "post",
url: `https://${this.shopify_developer_app.$auth.shop_id}.myshopify.com/admin/api/2024-04/graphql.json`,
headers: {
"X-Shopify-Access-Token": `${this.shopify_developer_app.$auth.access_token}`,
"Content-Type": `application/json`,
},
data,
})
},
})
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}}