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Each phase builds systematically on previous knowledge, with practical examples and clear explanations.\r\n\r\n### 🎯 Who Is This For?\r\n\r\n- 🔰 **Beginners** looking to build a solid foundation in NumPy\r\n- 🚀 **Intermediate users** wanting to deepen their understanding of advanced features\r\n- 🎓 **Students** preparing for data science, machine learning, or AI coursework\r\n- 💼 **Professionals** transitioning to roles requiring numerical computation skills\r\n\r\n## ⚡ Quick Start\r\n\r\nFor those familiar with Python environments, get started immediately:\r\n\r\n```bash\r\ngit clone https://github.com/Sourabh-Kumar04/Numpy-Basic.git\r\ncd Numpy-Basic\r\npython -m venv venv \u0026\u0026 source venv/bin/activate  # On Windows: venv\\Scripts\\activate\r\npip install -r requirements.txt\r\njupyter notebook\r\n```\r\n\r\nBegin with `Phase_1/01_phase_1.ipynb` and progress through each phase sequentially.\r\n\r\n## 📚 Learning Path\r\n\r\n\u003cdiv align=\"center\"\u003e\r\n  \u003cimg src=\"https://mermaid.ink/img/pako:eNp1kk1vgzAMhv9KlBOTOPQwCVpKdpgELPSwHvhQSIaqIYRkLpWG-O8LiQpDXQ6W_eT1h22QdpU0BJkJOQxlxWFn3d7YWukqVBqcg18MaIYDU2xVUsMjUCKZHqN51Ep48OG6Gw9Y4CjwZrIQPMgCi-VNRH8TqA33TgVaxN-ey1J0i_jD6Zx7zQlKTLd7U0qFnG3Ax-PxJigVKGuNhXvVusdHnTeLr4eLwOxnc4FXjCRPfUQPwqEQnJnXwXb6HQwU2ktrLKm_k-4WMBn-bZkWjqwOuq5CdYb9cQRXTukX9Xds0LlOlTSaBq4F5XfNZlGEF88o1WM5XEZ1ljGUEa71gMqZ27W7IWxAapDo9HWx6pA9ZymK3lSH9EIpx35ckadm0TblUwuXZEEFTb6wNxZlJN09XiZN?\" alt=\"NumPy Learning Path\" width=\"700\"/\u003e\r\n\u003c/div\u003e\r\n\r\n### 🔍 Phase-by-Phase Progress\r\n\r\n| Phase | Status | Topics | Key Concepts |\r\n|-------|--------|--------|-------------|\r\n| **🧩 Phase 1** | ✅ Complete | NumPy Fundamentals | Arrays vs Lists, Creating Arrays, Data Types, Basic Operations |\r\n| **📍 Phase 2** | ✅ Complete | Data Manipulation | Indexing, Slicing, Sorting, Boolean Masks, Fancy Indexing |\r\n| **🔄 Phase 3** | ✅ Complete | Array Transformation | Reshaping, Stacking, Splitting, Broadcasting Rules |\r\n| **🧮 Phase 4** | 🚧 In Progress | Advanced Topics | Vector/Matrix Operations, Trigonometric Functions, Statistics, File Operations |\r\n\r\n## 📁 Repository Structure\r\n\r\n```\r\nNumpy-Basic/\r\n├── LICENSE                         # Apache 2.0 License\r\n├── README.md                       # Project documentation\r\n├── main.py                         # Example runner script\r\n├── pyproject.toml                  # Project dependencies and configuration\r\n├── uv.lock                         # Dependency lock file (for uv users)\r\n├── Phase_1/\r\n│   └── 01_phase_1.ipynb            # NumPy Basics: Arrays, Creation, Types\r\n├── Phase_2/\r\n│   └── 01_phase_2.ipynb            # Data Access: Indexing, Slicing, Filtering\r\n├── Phase_3/\r\n│   └── 01_phase_3.ipynb            # Data Transformation: Reshaping, Stacking, Broadcasting\r\n└── Phase_4/\r\n    ├── 01_phase_4.ipynb            # Advanced: Math Operations, Statistics, Visualization\r\n    ├── array1.npy                  # Sample data file for practice\r\n    ├── array2.npy                  # Sample data file for practice\r\n    ├── array3.npy                  # Sample data file for practice\r\n    └── numpy_logo.npy              # NumPy logo encoded as an array\r\n```\r\n\r\n## 🛠️ Installation \u0026 Setup\r\n\r\n### Prerequisites\r\n\r\n- Python 3.8+ installed\r\n- Git (for cloning the repository)\r\n- Basic familiarity with Python programming\r\n\r\n### Step-by-Step Setup\r\n\r\n1. **Clone the repository**\r\n\r\n```bash\r\ngit clone https://github.com/Sourabh-Kumar04/Numpy-Basic.git\r\ncd Numpy-Basic\r\n```\r\n\r\n2. **Set up a virtual environment** (Choose your preferred method)\r\n\r\n```bash\r\n# Option 1: Standard venv\r\npython -m venv venv\r\nsource venv/bin/activate  # On macOS/Linux\r\n# OR\r\nvenv\\Scripts\\activate     # On Windows\r\n\r\n# Option 2: Using uv (faster alternative)\r\nuv venv\r\nuv activate\r\n```\r\n\r\n3. **Install dependencies**\r\n\r\n```bash\r\n# Option 1: Using pip\r\npip install -r requirements.txt\r\n\r\n# Option 2: Using uv with pyproject.toml\r\nuv pip install -e .\r\n```\r\n\r\n4. **Launch Jupyter Notebook**\r\n\r\n```bash\r\njupyter notebook\r\n```\r\n\r\n## 📖 What You'll Learn\r\n\r\n### 🧩 Phase 1: NumPy Fundamentals\r\n\r\n\u003cdiv align=\"center\"\u003e\r\n  \u003cimg src=\"https://user-images.githubusercontent.com/1671563/154354937-2d910d39-2a43-4b10-b35a-9fd7097bcb15.png\" alt=\"NumPy Array Illustration\" width=\"400\"/\u003e\r\n\u003c/div\u003e\r\n\r\n- Why NumPy over standard Python lists?\r\n  - Performance benchmarks showing speed differences\r\n  - Memory efficiency comparisons\r\n  - Vectorized operations\r\n- Creating arrays from different sources\r\n  - From Python lists\r\n  - Using built-in functions: `zeros()`, `ones()`, `arange()`, `linspace()`\r\n  - Random number generation\r\n- Understanding array data types and properties\r\n\r\n### 📍 Phase 2: Data Manipulation\r\n\r\n- Accessing array elements\r\n  - Basic indexing vs fancy indexing\r\n  - Difference between views and copies\r\n- Slicing multi-dimensional arrays\r\n- Advanced selection with boolean masks\r\n  - Filtering data with conditions\r\n  - Combining multiple conditions\r\n- Practical comparison between `np.where()` and boolean indexing\r\n- Sorting arrays and finding unique values\r\n\r\n### 🔄 Phase 3: Array Transformation\r\n\r\n- Inspecting array properties\r\n  - Shape, size, dimensions, data type\r\n- Reshaping arrays\r\n  - `reshape()`, `ravel()`, `flatten()`\r\n  - Adding/removing dimensions with `newaxis` and `squeeze()`\r\n- Combining arrays\r\n  - Vertical stacking with `vstack()`\r\n  - Horizontal stacking with `hstack()`\r\n  - General stacking with `concatenate()`\r\n- Broadcasting rules and compatibility\r\n  - When operations work between arrays of different shapes\r\n  - Common broadcasting errors and how to fix them\r\n\r\n### 🧮 Phase 4: Applications \u0026 Advanced Features\r\n\r\n- Vector, matrix, and tensor operations\r\n  - Dot products, cross products, matrix multiplication\r\n  - Linear algebra operations\r\n- Comprehensive angle function reference\r\n  - Trigonometric functions (`sin`, `cos`, `tan`)\r\n  - Inverse trigonometric functions (`arcsin`, `arccos`, `arctan2`)\r\n- Statistical functions for data analysis\r\n  - Measures of central tendency\r\n  - Measures of dispersion\r\n  - Percentiles and quantiles\r\n- Working with NumPy's native file formats\r\n  - `.npy` for single arrays\r\n  - `.npz` for multiple arrays\r\n- Data visualization with matplotlib\r\n\r\n## 📊 Code Examples\r\n\r\n### Performance Comparison: Lists vs. NumPy Arrays\r\n\r\n```python\r\nimport numpy as np\r\nimport time\r\n\r\n# Python list operation\r\nstart = time.time()\r\npython_list = list(range(1000000))\r\npython_list = [x * 2 for x in python_list]\r\nlist_time = time.time() - start\r\n\r\n# NumPy array operation\r\nstart = time.time()\r\nnumpy_array = np.arange(1000000)\r\nnumpy_array = numpy_array * 2\r\nnumpy_time = time.time() - start\r\n\r\nprint(f\"Python list processing time: {list_time:.5f} seconds\")\r\nprint(f\"NumPy array processing time: {numpy_time:.5f} seconds\")\r\nprint(f\"NumPy is {list_time/numpy_time:.1f}x faster!\")\r\n```\r\n\r\n\u003cdetails\u003e\r\n\u003csummary\u003eOutput\u003c/summary\u003e\r\n\r\n```\r\nPython list processing time: 0.12345 seconds\r\nNumPy array processing time: 0.00567 seconds\r\nNumPy is 21.8x faster!\r\n```\r\n\u003c/details\u003e\r\n\r\n### Fancy Indexing \u0026 Masking\r\n\r\n```python\r\nimport numpy as np\r\n\r\n# Create sample data\r\ndata = np.random.randint(0, 100, size=(5, 5))\r\nprint(\"Original data:\")\r\nprint(data)\r\n\r\n# Boolean masking (values greater than 50)\r\nmask = data \u003e 50\r\nfiltered_data = data[mask]\r\nprint(\"\\nValues greater than 50:\")\r\nprint(filtered_data)\r\n\r\n# Using np.where() for conditional values\r\nresult = np.where(data \u003e 50, data * 2, data)\r\nprint(\"\\nValues \u003e 50 doubled, others unchanged:\")\r\nprint(result)\r\n```\r\n\r\n### Visualization with Dark Mode\r\n\r\n```python\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\n# Set dark style\r\nplt.style.use('dark_background')\r\n\r\n# Generate data\r\nx = np.linspace(0, 2 * np.pi, 100)\r\ny1 = np.sin(x)\r\ny2 = np.cos(x)\r\n\r\n# Create plot\r\nplt.figure(figsize=(10, 6))\r\nplt.plot(x, y1, label='sin(x)', color='cyan', linewidth=2)\r\nplt.plot(x, y2, label='cos(x)', color='magenta', linewidth=2)\r\nplt.title(\"Trigonometric Functions\", fontsize=16)\r\nplt.xlabel(\"x (radians)\", fontsize=12)\r\nplt.ylabel(\"Amplitude\", fontsize=12)\r\nplt.legend(fontsize=12)\r\nplt.grid(True, alpha=0.3)\r\nplt.tight_layout()\r\nplt.show()\r\n```\r\n\r\n\u003cdiv align=\"center\"\u003e\r\n  \u003cimg src=\"https://user-images.githubusercontent.com/1671563/157742658-5a197551-29a4-4f56-8e6c-ed0146fa9dc3.png\" alt=\"Dark Mode Visualization Example\" width=\"600\"/\u003e\r\n\u003c/div\u003e\r\n\r\n## 📈 Statistical Functions Reference\r\n\r\n| Function | Description | Example |\r\n|----------|-------------|---------|\r\n| `np.mean()` | Arithmetic mean | `np.mean(arr, axis=0)` |\r\n| `np.median()` | Median value | `np.median(arr)` |\r\n| `np.std()` | Standard deviation | `np.std(arr, ddof=1)` |\r\n| `np.var()` | Variance | `np.var(arr)` |\r\n| `np.min()` | Minimum value | `np.min(arr, axis=1)` |\r\n| `np.max()` | Maximum value | `np.max(arr)` |\r\n| `np.percentile()` | nth percentile | `np.percentile(arr, 75)` |\r\n| `np.quantile()` | nth quantile | `np.quantile(arr, [0.25, 0.5, 0.75])` |\r\n| `np.corrcoef()` | Correlation coefficient | `np.corrcoef(x, y)` |\r\n| `np.cov()` | Covariance matrix | `np.cov(x, y)` |\r\n\r\n## 💾 Working with .npy Files\r\n\r\n```python\r\nimport numpy as np\r\n\r\n# Create sample array\r\narray = np.random.normal(0, 1, size=(100, 100))\r\n\r\n# Save to .npy file\r\nnp.save('sample_array.npy', array)\r\n\r\n# Load from .npy file\r\nloaded_array = np.load('sample_array.npy')\r\n\r\n# Verify it's the same\r\nprint(\"Arrays are identical:\", np.array_equal(array, loaded_array))\r\n```\r\n\r\n## 🤔 Common Questions \u0026 Answers\r\n\r\n\u003cdetails\u003e\r\n\u003csummary\u003e\u003cb\u003eWhy use NumPy instead of Python lists?\u003c/b\u003e\u003c/summary\u003e\r\nNumPy arrays are more efficient than Python lists for numerical operations because:\r\n\u003cul\u003e\r\n  \u003cli\u003eThey store data in contiguous memory blocks\u003c/li\u003e\r\n  \u003cli\u003eThey leverage vectorized operations (SIMD instructions)\u003c/li\u003e\r\n  \u003cli\u003eThey offer specialized numerical functions optimized in C\u003c/li\u003e\r\n  \u003cli\u003eThey use less memory for the same amount of numerical data\u003c/li\u003e\r\n\u003c/ul\u003e\r\n\u003c/details\u003e\r\n\r\n\u003cdetails\u003e\r\n\u003csummary\u003e\u003cb\u003eWhat's the difference between a view and a copy?\u003c/b\u003e\u003c/summary\u003e\r\n\u003cul\u003e\r\n  \u003cli\u003eA \u003cb\u003eview\u003c/b\u003e is just a different way to access the same data - changes to the view affect the original array\u003c/li\u003e\r\n  \u003cli\u003eA \u003cb\u003ecopy\u003c/b\u003e is a new array with the same values - changes to the copy don't affect the original\u003c/li\u003e\r\n  \u003cli\u003eBasic slicing typically returns views, while advanced indexing returns copies\u003c/li\u003e\r\n\u003c/ul\u003e\r\n\u003c/details\u003e\r\n\r\n\u003cdetails\u003e\r\n\u003csummary\u003e\u003cb\u003eWhat are broadcasting rules?\u003c/b\u003e\u003c/summary\u003e\r\nBroadcasting allows NumPy to perform operations on arrays of different shapes. The rules are:\r\n\u003cul\u003e\r\n  \u003cli\u003eArrays are compared from their trailing dimensions\u003c/li\u003e\r\n  \u003cli\u003eDimensions with size 1 are stretched to match the other array\u003c/li\u003e\r\n  \u003cli\u003eMissing dimensions are treated as having size 1\u003c/li\u003e\r\n  \u003cli\u003eIf dimensions are compatible, broadcasting proceeds\u003c/li\u003e\r\n\u003c/ul\u003e\r\n\u003c/details\u003e\r\n\r\n## 📚 Additional Resources\r\n\r\n- [Official NumPy Documentation](https://numpy.org/doc/stable/)\r\n- [NumPy Cheat Sheet (PDF)](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Numpy_Python_Cheat_Sheet.pdf)\r\n- [From Python to NumPy](https://www.labri.fr/perso/nrougier/from-python-to-numpy/)\r\n- [NumPy Tutorials](https://numpy.org/numpy-tutorials/)\r\n- [100 NumPy Exercises](https://github.com/rougier/numpy-100)\r\n\r\n## 🧠 How to Contribute\r\n\r\nContributions to improve this repository are welcome! Here's how you can help:\r\n\r\n1. **Fork** the repository\r\n2. **Create a branch** for your feature or fix\r\n3. **Commit** your changes with descriptive messages\r\n4. **Push** to your branch\r\n5. Submit a **Pull Request**\r\n\r\n### Commit Message Convention\r\n\r\n```\r\ngit commit -m \"✨ Added Phase_3: Array reshaping and broadcasting examples\"\r\n```\r\n\r\n| Emoji | Description |\r\n|-------|-------------|\r\n| ✨ | New features or content |\r\n| 🐛 | Bug fixes |\r\n| 📝 | Documentation updates |\r\n| 🔧 | Configuration changes |\r\n| 🧹 | Code cleanup |\r\n| 🎨 | Style improvements |\r\n\r\n## 💬 Community \u0026 Support\r\n\r\n- **GitHub Discussions**: [Open a discussion](https://github.com/Sourabh-Kumar04/Numpy-Basic/discussions)\r\n- **Issue Tracker**: [Report bugs or request features](https://github.com/Sourabh-Kumar04/Numpy-Basic/issues)\r\n\r\n## 📄 License\r\n\r\nThis project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.\r\n\r\n## 📊 Citation\r\n\r\nIf you use this repository in your research or educational materials, please cite it as:\r\n\r\n```bibtex\r\n@misc{kumar2025numpybasic,\r\n  author = {Kumar, Sourabh},\r\n  title = {NumPy-Basic: Comprehensive Guide to NumPy Fundamentals},\r\n  year = {2025},\r\n  publisher = {GitHub},\r\n  url = {https://github.com/Sourabh-Kumar04/Numpy-Basic},\r\n  howpublished = {\\url{https://github.com/Sourabh-Kumar04/Numpy-Basic}},\r\n}\r\n```\r\n\r\n## 🌐 Connect \u0026 Support\r\n\r\n- **Author**: Sourabh Kumar\r\n- **GitHub**: [@Sourabh-Kumar04](https://github.com/Sourabh-Kumar04)\r\n- **LinkedIn**: [linkedin.com/in/sourabh-kumar04](https://linkedin.com/in/sourabh-kumar04)\r\n\r\n---\r\n\r\n\u003cdiv align=\"center\"\u003e\r\n  \u003cp\u003eIf you find this repository helpful, please consider starring it! ⭐️\u003c/p\u003e\r\n  \u003cp\u003e\r\n    \u003ca href=\"https://buymeacoffee.com/                    buymeacoffee.com/Sourabh_Kumar04\"              target=\"_blank\"\u003e\r\n      \u003cimg src=\"https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png\" alt=\"Buy Me A Coffee\" height=\"40px\"\u003e\r\n    \u003c/a\u003e\r\n  \u003c/p\u003e\r\n  \u003cp\u003e\"NumPy doesn't just compute numbers—it transforms how we think about data.\" 🧮\u003c/p\u003e\r\n\u003c/div\u003e\r\n","funding_links":["https://buymeacoffee.com/"],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsourabh-kumar04%2Fnumpy-basic","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsourabh-kumar04%2Fnumpy-basic","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsourabh-kumar04%2Fnumpy-basic/lists"}