Django vs NumPy — A Clear Comparison for Beginners

# Django — a simple model and view

from django.db import models
from django.http import JsonResponse

class Post(models.Model):
    title = models.CharField(max_length=200)
    body  = models.TextField()
    created_at = models.DateTimeField(auto_now_add=True)

def post_list(request):
    posts = Post.objects.all().values()
    return JsonResponse(list(posts), safe=False)

# Django REST Framework — API view

from rest_framework import serializers, viewsets
from .models import Post

class PostSerializer(serializers.ModelSerializer):
    class Meta:
        model  = Post
        fields = ["id", "title", "body", "created_at"]

class PostViewSet(viewsets.ModelViewSet):
    queryset = Post.objects.all()
    serializer_class = PostSerializer

# URL: /api/posts/ → full CRUD API automatically

# NumPy — arrays and vectorised operations

import numpy as np

# Create arrays
a = np.array([1, 2, 3, 4, 5])
b = np.arange(1, 6)         # [1, 2, 3, 4, 5]
m = np.zeros((3, 3))        # 3x3 zeros
r = np.random.rand(3, 3)    # 3x3 random

# Vectorised math (no loops!)
c = a * 2                    # [2, 4, 6, 8, 10]
d = a + b                    # [2, 4, 6, 8, 10]
e = a ** 2                   # [1, 4, 9, 16, 25]

# Stats
print(a.mean())              # 3.0
print(a.std())               # 1.41...
print(a.sum())               # 15

# Boolean indexing
print(a[a > 2])              # [3, 4, 5]

# NumPy — linear algebra

import numpy as np

# Matrix multiplication
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])

C = A @ B                    # [[19, 22], [43, 50]]
print(C)

# Transpose
print(A.T)                   # [[1, 3], [2, 4]]

# Inverse and determinant
print(np.linalg.inv(A))
print(np.linalg.det(A))      # -2.0

# Solve linear system: Ax = b
b = np.array([1, 2])
x = np.linalg.solve(A, b)
print(x)

# These operations power ML models like
# linear regression, PCA, and neural networks.

Django Path — 6 months (Web Development):

Month 1: Python Basics
  ☐ Variables, functions, classes, imports
  ☐ File handling and virtual environments
  ☐ pip and project setup

Month 2: Django Basics
  ☐ Project structure, settings
  ☐ Models and migrations
  ☐ Views, URLs, and templates

Month 3: Admin & Auth
  ☐ Django admin customisation
  ☐ User authentication
  ☐ Forms and validation

Month 4: Django REST Framework
  ☐ Serializers, viewsets, routers
  ☐ Token / JWT auth
  ☐ Building real APIs

Month 5: Full-Stack Project
  ☐ Build a full CRUD app
  ☐ Connect a frontend (React or templates)
  ☐ Deploy on Render / Railway

Month 6: Portfolio & Interviews
  ☐ 2-3 deployed Django projects
  ☐ GitHub with clean README
  ☐ Practice Django interview questions

NumPy Path — 3 months (Data Science foundation):

Month 1: Python & NumPy Basics
  ☐ Install Anaconda or use Jupyter
  ☐ NumPy arrays and shapes
  ☐ Indexing, slicing, reshaping
  ☐ Vectorised operations

Month 2: NumPy for Data Work
  ☐ Aggregations (mean, std, sum)
  ☐ Broadcasting and axis operations
  ☐ Random sampling and simulations
  ☐ Linear algebra with linalg

Month 3: Extend to Pandas & Plots
  ☐ Install Pandas and Matplotlib
  ☐ Convert NumPy arrays to DataFrames
  ☐ Basic data cleaning and plotting
  ☐ Build a mini analysis project
     (e.g., sales analysis or survey data)

Next steps after NumPy:
  → Pandas (data manipulation)
  → Matplotlib / Seaborn (visualisation)
  → scikit-learn (classical ML)
  → Deep learning (PyTorch / TensorFlow)