Working with Multidimensional Arrays
NumPy goes beyond 1D and 2D — it also supports 3D arrays and higher dimensions.
Each added dimension increases the level of nesting and structural complexity.
You’ll most often encounter 3D arrays when working with image data, videos, or time-series batches.
Dimensions and Shape
- A 1D array has a shape like
(3,) - A 2D array might be
(2, 3) - A 3D array could look like
(2, 3, 4), meaning 2 blocks, each with 3 rows and 4 columns
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