🔢 NumPy Lab (Comprehensive 75-Topic Masterclass)

python • numpy • jupyter-notebook • linear-algebra • scientific-computing • matrix-decomposition • colab

An interactive 75-topic masterclass repository for numerical computing, advanced linear algebra, matrix decomposition, Fourier transforms, and tensor manipulations using NumPy on Google Colab or JupyterLab.

---

Tangkapan Layar & Pratinjau Interaktif

---

📌 Curriculum & Core Learning Modules

• Array Creation Fundamentals: NumPy initialization, 1D/2D/3D array construction, dimensional attributes (ndim, shape, size, dtype), array constructors (zeros, ones, empty, full, arange, linspace), and pseudo-random generators (rand, randn, randint).
• Indexing, Slicing & Selection: Multidimensional indexing, matrix slicing, boolean masking, fancy indexing, conditional filters (np.where), positional searches (argwhere, nonzero), extrema discovery (argmax, argmin), and sorting (argsort).
• Shape & Dimensional Transformations: Reshaping (reshape), memory flattening (ravel, flatten), transposition (.T), axis expansion (newaxis, expand_dims), squeeze operations, concatenation, multidimensional stacking (vstack, hstack), splitting, and array mutations (append, insert, delete).
• Mathematical & Statistical Computing: Element-wise arithmetic, multidimensional broadcasting, universal functions (sqrt, exp), trigonometry (sin, cos, tan), logarithmic transforms, aggregations (sum, mean, median), variance & standard deviation (var, std), axis reductions, numerical rounding, and matrix multiplications (dot, @).
• Linear Algebra & Memory Management: Unique values (unique), set logic (intersect1d, union1d), NaN auditing (isnan, nanmean), matrix inversion (linalg.inv), determinants (linalg.det), eigensystems (linalg.eig), linear solvers (linalg.solve), I/O persistence (savetxt, loadtxt, .npy), and memory view vs. copy semantics.
• Advanced Scientific Computing: Singular Value Decomposition (linalg.svd), QR Decomposition (linalg.qr), Cholesky Decomposition (linalg.cholesky), vector & matrix norms ($L_1$, $L_2$, Frobenius), condition numbers (linalg.cond), Moore-Penrose pseudoinverse (linalg.pinv), polynomial curve fitting (np.polyfit), 1D Fast Fourier Transform (np.fft.fft), 1D/2D histograms, cumulative scans (cumsum, cumprod), range clipping (np.clip), trace & diagonal extractions, triangular matrices (triu, tril), outer & Kronecker products, Einstein summation convention (np.einsum), masked arrays (numpy.ma), structured record arrays, 2D coordinate meshgrids (np.meshgrid), numerical vectorization (np.vectorize), numerical gradients (np.gradient), linear interpolation (np.interp), and bitwise operations.

---

🚀 Execution & Setup Instructions

Option A: Google Colab

Click the Open in Colab badge above or open belajar-numpy.ipynb directly in Google Colab.

Option B: Local JupyterLab Environment

# Clone repository
git clone https://github.com/pranataa-labs/NumPy-Lab.git
cd NumPy-Lab

# Install dependencies
pip install -r requirements.txt

# Launch JupyterLab
jupyter lab

---

Crafted with precision by pranataa-labs.