NumPy Fundamentals

A complete, structured practice repository covering NumPy from basics to advanced operations — built as part of a 3-year hands-on journey in Machine Learning and Data Science.

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📌 About This Repository

This repository contains well-organized Python scripts covering all core NumPy concepts. Each file is focused on one topic, numbered in learning order, and written with clear comments for easy understanding.

NumPy is the backbone of all scientific computing in Python — mastering it is the first step toward Machine Learning, Deep Learning, and Data Science.

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📁 Repository Structure

numpy-fundamentals/
│
├── basics/
│   ├── 01_array_creation.py        # Creating arrays: ones, zeros, eye, linspace, arange, random
│   ├── 02_arithmetic_operations.py # Add, subtract, multiply, divide on arrays
│   ├── 03_arithmetic_functions.py  # NumPy math functions: sin, cos, sqrt, log, exp
│   ├── 04_shape_reshape.py         # Shape, reshape, flatten, transpose
│   ├── 05_indexing.py              # Array indexing — 1D and 2D
│   ├── 06_slicing.py               # Array slicing — 1D and 2D
│   ├── 07_type_casting.py          # dtype, astype, type conversion
│   ├── 08_iteration.py             # Iterating arrays with nditer
│   ├── 09_copy_vs_view.py          # Difference between copy and view
│   ├── 10_join_split.py            # Concatenate, stack, split arrays
│   ├── 11_numpy_functions.py       # Statistical functions: sum, mean, min, max, std
│   ├── 12_matrix_operations.py     # Matrix creation, multiplication, determinant
│   └── 13_insert_delete.py         # Insert, delete, append elements
│
├── .gitignore
├── requirements.txt
└── README.md

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🧠 Topics Covered

#	Topic	Key Concepts
01	Array Creation	ones, zeros, eye, linspace, arange, random
02	Arithmetic Operations	add, subtract, multiply, divide, mod, power
03	Arithmetic Functions	Math functions on arrays
04	Shape & Reshape	shape, reshape, flatten, ravel, transpose
05	Indexing	1D/2D indexing, fancy indexing
06	Slicing	1D/2D slicing with step
07	Type Casting	dtype, astype, type conversion
08	Iteration	nditer, ndenumerate
09	Copy vs View	Memory behavior, deep vs shallow copy
10	Join & Split	concatenate, stack, hstack, vstack, split
11	NumPy Functions	sum, mean, min, max, std, var, sort
12	Matrix Operations	dot product, matrix multiply, determinant
13	Insert & Delete	insert, delete, append operations

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🛠️ Tech Stack

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🚀 How to Run

1. Clone the repository:

git clone https://github.com/Arifkhan171/numpy-fundamentals.git
cd numpy-fundamentals

2. Install dependencies:

pip install -r requirements.txt

3. Run any script:

python basics/01_array_creation.py

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👤 Author

Arif Khan Final Year CS Student | University of Loralai | ML • Deep Learning • Agentic AI

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⭐ Part of a complete Data Science & AI portfolio — from NumPy basics to Agentic AI systems.