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## What kind of change does this PR introduce? Frontmatter name change. ## What is the current behavior? We repeatedly mistake `thumb` for `image` and visa versa, meaning the wrong images are used for Open Graph and in-site thumbnails on blog posts. Events and case studies use the same naming convention too. ## What is the new behavior? These two bits of frontmatter are renamed for clarity: - Blog posts: `imgThumb` + `imgSocial` That mapping for blog posts: - `thumb` is now `imgThumb` - `image` is now `imgSocial` These related bits remain as-is: - Events - Case studies The [www/README.md](https://github.com/supabase/supabase/blob/dnywh/chore/blog-image-frontmatter/apps/www/README.md#best-practices) file has been expanded to clarify all of the above. It now also provides instructions on image optimisation. ## To test A lot of files were touched here. Please help make sure: - [ ] The CMS works as intended. This is the **biggest unknown**. - [x] All blog posts render the correct image as their on-site thumbnail and Open Graph image. You can test the latter by firing up a draft iMessage. Online Open Graph services like Facebook cache images, so aren’t reliable. - [x] All events render their correct images - [x] All case studies render their correct images - [x] All customer stories render their correct images ([known issue](https://supabase.slack.com/archives/C072FL5KKKP/p1768888063209359?thread_ts=1768885681.502169&cid=C072FL5KKKP), predates this work)
191 lines
7.9 KiB
Plaintext
191 lines
7.9 KiB
Plaintext
---
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title: Python data loading with Supabase
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description: An example of how to load data into Supabase using supabase-py
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author: ant_wilson
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imgSocial: python-1/python.jpg
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imgThumb: python-1/python.jpg
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categories:
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- engineering
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tags:
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- python
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- open-source
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- community
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date: '2022-06-17'
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toc_depth: 3
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---
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Python is an excellent choice when it comes to data science. With a wide selection of libraries and built-in analytics tools, you can crunch data with ease, analyze even the most complex datasets, and visualize your results in beautiful charts and graphs.
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Supabase is backend-as-a-service built on top of PostgreSQL. It's an excellent choice for building modern data-intensive apps and tooling.
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Thanks to our incredible community, Supabase now has a powerful and open source [Python SDK](https://github.com/supabase-community/supabase-py). With Supabase and Python, you can automate tasks such as CRUD operations with only a few lines of code. This guide will first create a simple schema in Supabase, then we'll use the Supabase Python SDK to show how you can load sample data.
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## Prerequisites
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Before we dive in, let's look at some prerequisites you'll need:
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- Python version > 3.7
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- The SDK only supports version > 3.7. You can download a supported Python version from [here](https://www.python.org/downloads/).
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- Python virtual environment
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- This is optional, but it will avoid issues of package dependencies and version conflicts. You can find the steps to create a virtual environment [here](https://uoa-eresearch.github.io/eresearch-cookbook/recipe/2014/11/26/python-virtual-env/). We will be using PyCharm to harness its venv creation capabilities.
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- Faker python package
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- We will be using the [faker-commerce](https://pypi.org/project/faker-commerce/) package from the Faker library in Python to generate realistic sample data.
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## Loading data into Supabase using Python
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Supabase is built for developers, and you can [get started for free](https://supabase.com/dashboard) using your existing GitHub account. Once your Supabase account is set up, you will access the Supabase dashboard. From here, go to All Project > New Project.
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Give your project a name and set the database password. You can also choose the region and adjust the pricing plan based on the requirements of your project.
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Your project will spin up within 2 minutes.
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## Creating tables in Supabase
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In this example, we'll be creating 2 tables in Supabase:
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- Vendor (fields are vendor_name, vendor_location, and total_employees)
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- Product (vendor_id as FK, product_name, price, and total orders)
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The database schema will look like the following:
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Let us now begin creating the tables. Once you create a project, you will need to go to Table Editor > New Table
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Now, you can create a table according to the defined schema.
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Click on Save to create your vendor table. Similarly, create the product table.
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Before you click Save, you need to set up the foreign key relationship between the Product and Vendor table. To do this, select the button next to “vendor_id”
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Select the vendor_id primary key from the “Vendor” table.
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Click on Save and you are good to go. You should now see the 2 tables under Table Editor.
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## Installing the Python SDK
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Once you have set up the tables and installed the prerequisites, you can now start playing around with the [Python SDK](https://github.com/supabase-community/supabase-py). To install the SDK, run the following command:
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`pip3 install supabase`
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Ensure that you are running this inside your python virtual environment. This will take a few minutes to complete.
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## Using Supabase API keys
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The SDK authentication uses API keys pointing to a project URL. To find out your project URL and APIs, go to Settings > API.
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## Setting up the environment variables
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API credentials and project URL can be stored in environment variables. Setting the environment variables in bash/zsh is very simple. All we need to do is run:
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```bash
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export <variable_name>=<variable_value>
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```
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So for our example we will set them up like this:
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```bash
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export SUPABASE_URL=<<the value under config > URL>>
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export SUPABASE_KEY=<<the value present in Project API keys > anon public>>
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export SUPABASE_SECRET_KEY=<<the value present in Project API keys > service_role secret>>
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```
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## Inserting data into Supabase
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Here is a snippet of the code we will be using to insert random data into our tables:
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```python
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import os
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import json
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from dotenv import load_dotenv
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from supabase import create_client, Client
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from faker import Faker
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import faker_commerce
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def add_entries_to_vendor_table(supabase, vendor_count):
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fake = Faker()
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foreign_key_list = []
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fake.add_provider(faker_commerce.Provider)
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main_list = []
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for i in range(vendor_count):
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value = {'vendor_name': fake.company(), 'total_employees': fake.random_int(40, 169),
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'vendor_location': fake.country()}
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main_list.append(value)
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data = supabase.table('Vendor').insert(main_list).execute()
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data_json = json.loads(data.json())
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data_entries = data_json['data']
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for i in range(len(data_entries)):
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foreign_key_list.append(int(data_entries[i]['vendor_id']))
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return foreign_key_list
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def add_entries_to_product_table(supabase, vendor_id):
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fake = Faker()
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fake.add_provider(faker_commerce.Provider)
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main_list = []
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iterator = fake.random_int(1, 15)
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for i in range(iterator):
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value = {'vendor_id': vendor_id, 'product_name': fake.ecommerce_name(),
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'inventory_count': fake.random_int(1, 100), 'price': fake.random_int(45, 100)}
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main_list.append(value)
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data = supabase.table('Product').insert(main_list).execute()
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def main():
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vendor_count = 10
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load_dotenv()
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url: str = os.environ.get("SUPABASE_URL")
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key: str = os.environ.get("SUPABASE_KEY")
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supabase: Client = create_client(url, key)
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fk_list = add_entries_to_vendor_table(supabase, vendor_count)
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for i in range(len(fk_list)):
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add_entries_to_product_table(supabase, fk_list[i])
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main()
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```
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To summarize what we have done using this code snippet:
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- We have inserted 10 random vendors to the table.
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- For each of the 10 vendors, we have inserted a number of different products
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## Reading the data stored in Supabase
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Data can also be viewed directly from the Supabase dashboard. To do this, go to `Table Editor > All tables`
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Note: In case you cannot see any of the data, you should hit the Refresh button.
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## Conclusion
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With Python, data loading into Supabase is easy. It just takes a few easy steps to get started with the Python SDK and Supabase. In the next part of this blog series, we will learn [how to visualize the data that we just loaded into Supabase using Metabase](/blog/visualizing-supabase-data-using-metabase). Stay tuned!
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If you have any questions please reach out via [Twitter](https://twitter.com/supabase) or join our [Discord](https://discord.supabase.com).
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## More Python and Supabase resources
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- [supabase-py](https://github.com/supabase-community/supabase-py)
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- [Slack Consolidate: a slackbot built with Python and Supabase](/blog/slack-consolidate-slackbot-to-consolidate-messages)
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- [Supabase-py (Database) on Replit](https://replit.com/@Supabase/Supabase-py-Database?v=1)
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