Array Shapes, Axes, and Broadcasting

To work effectively with NumPy operations, it’s essential to understand shapes and axes — the core concepts that define array structure.

Every array has a .shape, which shows how many elements it has in each dimension.

For example, an array with 2 rows and 3 columns has a shape of (2, 3).

An axis represents the direction along which a NumPy function performs its operation.

• axis=0: down the rows (vertical)
• axis=1: across the columns (horizontal)

You will use axes with functions like sum(), mean(), and others.