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📚 Learn NumPy from Scratch
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Welcome to my journey of learning NumPy, the fundamental Python library for numerical computing!
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This repository is a collection of my personal notes, examples, and practice problems — organized and written from scratch as I explore NumPy.
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# 📘 NumPy Masterclass: Complete Guide to Numerical Computing with Python
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🚀 About This Repository
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📖 Learn NumPy step-by-step — starting from the basics to more advanced topics.
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Welcome to the **NumPy Masterclass** repository!
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This repo is your all-in-one resource to master NumPy — the powerful Python library used for fast and efficient numerical operations.
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🛠️ Simple examples and explanations to make NumPy concepts easy to understand.
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Whether you're a beginner in data science, a Python developer, or prepping for technical interviews, this hands-on guide will walk you through every essential NumPy topic, with practical examples and clean code in each section.
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🎯 Perfect for beginners who are just getting started with Python for Data Science, Machine Learning, or Deep Learning.
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---
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📦 Practical mini-projects and problem-solving exercises included.
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## 🚀 What You’ll Learn
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📌 Topics Covered
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Introduction to NumPy
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- How to create and manipulate NumPy arrays
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- Understand array properties and data types
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- Perform indexing, slicing, reshaping, and filtering
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- Use advanced operations like broadcasting, vectorization, and aggregation
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- Handle missing or invalid values in datasets
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- Build a real-world mini project using restaurant data
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Creating and Working with Arrays
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---
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Array Indexing and Slicing
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## 📚 Learning Path
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Array Operations (Mathematical, Logical, Statistical)
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The folders in this repository are arranged in a progressive learning order. Start from the top and work your way down:
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Reshaping, Flattening, and Resizing Arrays
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### 1️⃣ **📁 Creation**
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Learn how to create arrays using functions like `np.array`, `np.zeros`, `np.ones`, `np.arange`, and `np.linspace`. This is the foundation of all NumPy operations.
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Broadcasting
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---
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Working with Random Numbers
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### 2️⃣ **📁 Numpy Array Properties**
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Understand the basic properties of arrays including:
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- `shape`, `ndim`, `dtype`, `size`
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- Data type conversions
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- Memory layout of arrays
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Useful NumPy Functions
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Performance comparison with Python lists
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### 3️⃣ **📁 Indexing and Slicing**
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Master the core of data access using:
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- Basic and advanced indexing
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- Slicing 1D and 2D arrays
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- Fancy indexing
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- Boolean masking and conditional filters
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And more as I continue learning 🚀
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🧠 Why I Created This
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I believe that the best way to learn is by building and sharing.
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This repo is not just my notebook — it's a resource for anyone starting their NumPy journey!
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### 4️⃣ **📁 Reshaping and Manipulation**
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Learn to reshape arrays using:
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- `reshape`, `flatten`, `ravel`, `resize`
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- Stack and split arrays with `hstack`, `vstack`, `split`, `hsplit`, etc.
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- Insert, delete, and append values to arrays
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🌟 How You Can Use It
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Clone or fork the repository.
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Go through the notebooks or Python scripts at your own pace.
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### 5️⃣ **📁 Numpy Operations**
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Dive into powerful mathematical operations including:
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- Element-wise arithmetic
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- Aggregation functions: `sum`, `mean`, `min`, `max`, `std`, `var`
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- Sorting, comparisons, and statistical summaries
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Practice the exercises provided.
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Feel free to suggest improvements or contribute if you spot errors — let's learn together!
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### 6️⃣ **📁 Broadcasting and Vectorization**
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Learn how NumPy automatically expands smaller arrays to match larger shapes:
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- Broadcasting rules and use-cases
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- Replace loops with vectorized operations for better performance
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🔗 Connect
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---
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### 7️⃣ **📁 Handling Missing Values**
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Explore techniques to handle incomplete or invalid data:
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- Use of `np.nan` and `np.isnan()`
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- Replacing missing values
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- Filtering or imputing data
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### 8️⃣ **📁 Mini Project - Restaurant Dataset**
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Apply everything you've learned in a real-world scenario:
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- Load a restaurant dataset using NumPy
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- Clean and preprocess the data
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- Perform analysis and compute statistics
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- Generate insights using slicing, masking, and aggregation
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---
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## 🧠 Why Learn NumPy?
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NumPy is the **core library for scientific computing in Python**, and it's the foundation for other libraries like Pandas, SciPy, Scikit-learn, and TensorFlow. Mastering NumPy gives you a **huge head-start** in data science, machine learning, and AI.
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## ✅ Requirements
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- Python 3.x
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- NumPy (install via `pip install numpy`)
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## 📌 Tips for Best Learning
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- Clone the repo and run the code snippets locally.
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- Modify examples and observe how the results change.
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- Try out extra exercises at the end of each file.
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- Use Jupyter Notebooks for a more interactive experience.
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---
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## 📂 Folder Structure
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```bash
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numpy-masterclass/
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│
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├── creation/
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├── numpy-array-properties/
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├── indexing-and-slicing/
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├── reshaping-and-manipulation/
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├── numpy-operations/
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├── broadcasting-and-vectorization/
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├── handling-missing-values/
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└── mini-project-restaurant-data/

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