NumPy, Pandas, SciPy & Django Intros

NumPy provides fast numeric arrays (ndarray) and math operations on whole arrays at once.

NumPy — intro

NumPy provides fast numeric arrays (ndarray) and math operations on whole arrays at once.

Install: pip install numpy. Foundation for Pandas, SciPy, and ML libraries.

Real-life example: NumPy is a spreadsheet engine — multiply entire columns in one step, not cell by cell.

NumPy array

import numpy as np

arr = np.array([1, 2, 3, 4])
print(arr * 2)
print(arr.mean())

Pandas adds DataFrame tables — like Excel in Python. read_csv(), head(), describe() explore data.

Essential for data cleaning, analysis, and ML preprocessing.

Real-life example: Pandas is a smart ledger — rows are records, columns are fields, filters are one line.

Simple DataFrame

import pandas as pd

data = {"name": ["A", "B"], "score": [88, 92]}
df = pd.DataFrame(data)
print(df)
print(df["score"].mean())

SciPy builds on NumPy with scientific algorithms — optimization, statistics, signal processing.

Use scipy.stats for distributions and scipy.optimize for finding minima.

Real-life example: SciPy is the lab equipment room — advanced instruments built on NumPy's workbench.

SciPy stats

from scipy import stats

sample = [2, 4, 4, 4, 5, 5, 7, 9]
print(stats.mode(sample, keepdims=True))

Django is a full Python web framework — URLs, views, templates, ORM, admin panel.

Install: pip install django. django-admin startproject mysite starts a new site.

Real-life example: Django is a prefab house kit — walls, plumbing, and wiring included for web apps.

Minimal Django view idea

# views.py (concept)
from django.http import HttpResponse

def home(request):
    return HttpResponse("Hello from Django!")