WassupAI

Peek under the hood at how NumPy traverses memory. Understanding strides and memory layout (C vs. Fortran order) is the secret to optimizing performance and avoiding unnecessary data copying.

Extract insights from noise. We use aggregation functions to calculate histograms, correlations, and percentiles, rapidly summarizing millions of data points along specific axes.

Move beyond simple rand. We use the modern Generator API to sample from rigorous statistical distributions (Normal, Poisson), ensuring reproducible and scientifically valid simulations.

Dive into advanced linear algebra. We explore eigenvalues, singular value decomposition (SVD), and solving linear systems—tools essential for dimensionality reduction and physics simulations.

The engine of ML. We differentiate between dot products, inner/outer products, and matrix multiplication (matmul), utilizing the @ operator to transform vector spaces efficiently.

NumPy’s "magic" feature allows arithmetic between arrays of different shapes. We demystify the rules that align dimensions automatically, enabling concise code without manual data replication.

Perform element-wise math across entire arrays instantly. From basic arithmetic to trig functions (sin, exp), ufuncs are the key to NumPy’s speed, delegating heavy computation to optimized C-loops.

Learn to assemble complex datasets by stacking arrays vertically or horizontally using vstack and concatenate. We also cover splitting large arrays into manageable sub-sections for processing or validation.

Data rarely arrives in the right format. Master reshape, flatten, and expand_dims to alter array dimensions and geometry, ensuring your data aligns perfectly with the mathematical operations you need to perform.

Accessing data is foundational. We cover standard indexing, view-versus-copy semantics, and boolean masking to filter data based on conditions—critical for cleaning and preparing datasets for analysis.