Interactive NumPy Series - Universal Functions (ufuncs)
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.
Interactive NumPy Series - Combining and Splitting
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.
Interactive NumPy Series - Shape Manipulation
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.
Interactive NumPy Series - Indexing and Slicing
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.
Interactive NumPy Series - The NumPy ndarray Object
We examine the ndarray, NumPy’s core structure for efficient storage. You'll learn how contiguous memory layout and specific data types (dtype) enable vectorization, replacing slow loops with high-speed operations.
