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.

• A 1D array has 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

Use the whiteboard to explore the structure of 1D, 2D, and 3D arrays visually.