📘 NumPy Masterclass: Complete Guide to Numerical Computing with Python

Welcome to the NumPy Masterclass repository!
This repo is your all-in-one resource to master NumPy — the powerful Python library used for fast and efficient numerical operations.

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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🚀 What You’ll Learn

• How to create and manipulate NumPy arrays
• Understand array properties and data types
• Perform indexing, slicing, reshaping, and filtering
• Use advanced operations like broadcasting, vectorization, and aggregation
• Handle missing or invalid values in datasets
• Build a real-world mini project using restaurant data

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📚 Learning Path

The folders in this repository are arranged in a progressive learning order. Start from the top and work your way down:

1️⃣ 📁 Creation

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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2️⃣ 📁 Numpy Array Properties

Understand the basic properties of arrays including:

• shape, ndim, dtype, size
• Data type conversions
• Memory layout of arrays

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3️⃣ 📁 Indexing and Slicing

Master the core of data access using:

• Basic and advanced indexing
• Slicing 1D and 2D arrays
• Fancy indexing
• Boolean masking and conditional filters

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4️⃣ 📁 Reshaping and Manipulation

Learn to reshape arrays using:

• reshape, flatten, ravel, resize
• Stack and split arrays with hstack, vstack, split, hsplit, etc.
• Insert, delete, and append values to arrays

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5️⃣ 📁 Numpy Operations

Dive into powerful mathematical operations including:

• Element-wise arithmetic
• Aggregation functions: sum, mean, min, max, std, var
• Sorting, comparisons, and statistical summaries

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6️⃣ 📁 Broadcasting and Vectorization

Learn how NumPy automatically expands smaller arrays to match larger shapes:

• Broadcasting rules and use-cases
• Replace loops with vectorized operations for better performance

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7️⃣ 📁 Handling Missing Values

Explore techniques to handle incomplete or invalid data:

• Use of np.nan and np.isnan()
• Replacing missing values
• Filtering or imputing data

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8️⃣ 📁 Mini Project - Restaurant Dataset

Apply everything you've learned in a real-world scenario:

• Load a restaurant dataset using NumPy
• Clean and preprocess the data
• Perform analysis and compute statistics
• Generate insights using slicing, masking, and aggregation

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🧠 Why Learn NumPy?

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

• Python 3.x
• NumPy (install via pip install numpy)

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📌 Tips for Best Learning

• Clone the repo and run the code snippets locally.
• Modify examples and observe how the results change.
• Try out extra exercises at the end of each file.
• Use Jupyter Notebooks for a more interactive experience.

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📂 Folder Structure

numpy-masterclass/
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├── creation/
├── numpy-array-properties/
├── indexing-and-slicing/
├── reshaping-and-manipulation/
├── numpy-operations/
├── broadcasting-and-vectorization/
├── handling-missing-values/
└── mini-project-restaurant-data/

💡 Keep Exploring

Each topic is designed to be practical and example-driven. For deeper dives, check the official NumPy Documentation

📬 Feedback & Contributions

If you spot any errors or want to contribute new examples, feel free to raise an issue or open a pull request.

Happy Learning! 🚀