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📘 NumPy Tutorial Series

Welcome to my NumPy Tutorial Series repository!
This is a step-by-step learning journey where I practice and document everything about NumPy, starting from basics to advanced topics with hands-on examples.

🎯 What You’ll Learn

  • ✅ Basics of NumPy arrays and their properties
  • ✅ Indexing, slicing, and accessing elements
  • ✅ Mathematical operations and broadcasting
  • ✅ Reshaping, flattening, stacking, and splitting arrays
  • ✅ Random number generation with NumPy
  • ✅ Mathematical Operation
  • ✅ Statistical Functions
  • ✅ Linear Algebra
  • ✅ Numpy with Real Data
  • ✅ Load Data from CSV

📂 Repository Structure

  • Day1_Introduction.ipynb → Arrays, properties

  • Day2_Indexing_Slicing.ipynb → Indexing, slicing

  • Day3_Operations.ipynb → Arithmetic, broadcasting

  • Day4_Reshape_Stack_Split.ipynb → Reshape, flatten, stack, split

  • Day5_Random.ipynb → Random module

  • Day6_Mathematical.ipynb → Mathematical Operation

  • Day7_Statistical.ipynb → Statistical operation

  • Day8_Linearalgebra.ipynb → Linear Algebra

  • Day9_Realdata.ipynb → Numpy with Real Data

  • Day10_Loaddata.ipynb → Load data from CSV

🚀 Who Is This For?

  • Beginners learning NumPy from scratch
  • Data Science & Machine Learning students
  • Anyone preparing for coding interviews with Python + NumPy

🛠️ Requirements

  • Python 3.x
  • NumPy
  • Jupyter Notebook / JupyterLab

Install requirements using:

pip install numpy jupyter