📘 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.

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🎯 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

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📂 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

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🚀 Who Is This For?

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

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🛠️ Requirements

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

Install requirements using:

pip install numpy jupyter