Creating a Data Visualization Tool with Django ORM and FastAPI – Mia Bajić

Data visualization tools are essential for any organization looking to gain insights from their data. By combining the power of Django ORM and FastAPI, we can create a robust and efficient data visualization tool. In this tutorial, we will walk through the process of setting up a Django project, integrating FastAPI, and creating data visualization charts using popular libraries such as Plotly.

Step 1: Setting Up a Django Project
To get started, we need to create a new Django project. If you haven’t already installed Django, you can do so by running the following command:

pip install django

Next, create a new Django project by running:

django-admin startproject myproject

Navigate into the project directory:

cd myproject

Create a new Django app for our data visualization tool:

python manage.py startapp dataviz

Next, add the app to the INSTALLED_APPS list in the settings.py file:

INSTALLED_APPS = [
    ...
    'dataviz',
]

Step 2: Setting Up FastAPI
FastAPI is a modern web framework for building APIs with Python. To install FastAPI, run the following command:

pip install fastapi

Next, create a new file named api.py in the root directory of your Django project. This file will contain the FastAPI endpoints for our data visualization tool.

from fastapi import FastAPI
from fastapi.middleware.wsgi import WSGIProxyMiddleware
from myproject.wsgi import application

app = FastAPI()

@app.get("/data")
def get_data():
    # Your data retrieval code here
    return {"data": []}

# Mount the Django WSGI application
app.mount("/django", WSGIProxyMiddleware(application))

Step 3: Configuring Django ORM
Django ORM is a powerful tool for interacting with databases in Django projects. To configure Django ORM, update the DATABASES setting in the settings.py file:

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.sqlite3',
        'NAME': BASE_DIR / 'db.sqlite3',
    }
}

Next, create a model to represent the data you will be visualizing. For example, if you want to display sales data, your model could look like this:

from django.db import models

class Sale(models.Model):
    date = models.DateField()
    amount = models.DecimalField(max_digits=10, decimal_places=2)

Don’t forget to run migrations to apply the changes to the database:

python manage.py makemigrations
python manage.py migrate

Step 4: Creating Data Visualization Charts
To create data visualization charts, we will use the Plotly library. Install Plotly by running:

pip install plotly

Next, create a new view in your Django app that will generate the data for the charts:

from django.http import JsonResponse
import plotly.express as px

def visualize_data(request):
    # Retrieve data from the database
    sales = Sale.objects.all()

    # Create a Plotly figure
    fig = px.line(sales, x='date', y='amount', title='Sales Data')

    return JsonResponse(fig.to_dict())

Finally, create a URL pattern in the urls.py file of your Django app to point to the new view:

from django.urls import path
from .views import visualize_data

urlpatterns = [
    path('visualize/', visualize_data, name='visualize_data'),
]

Now you can access the data visualization charts by navigating to the /dataviz/visualize/ endpoint in your browser.

In this tutorial, we have explored how to combine Django ORM and FastAPI to create a data visualization tool. By leveraging the power of Django’s ORM for data retrieval and FastAPI for API endpoints, we can build a robust and efficient tool for visualizing data. The possibilities are endless, and with the right libraries and tools, you can create stunning data visualization charts to gain valuable insights from your data.