Working with Multidimensional Arrays
NumPy doesn't stop at 1D and 2D. It also supports 3D arrays and beyond.
Each additional dimension adds another level of nesting and shape complexity.
You'll usually encounter 3D arrays in areas like image data 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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