Beginner

What Is an Array?

ndarray vs. Python list, and the core ways to create one.

A Python list can hold anything and grow freely, but it's slow for math and doesn't understand "elementwise" operations. NumPy's core type, ndarray (n-dimensional array), trades that flexibility for speed: every element is the same fixed , packed contiguously in memory, so operations run as fast, C loops instead of a Python for loop.

That's the single most important mental shift coming from plain Python: operators act elementwise on arrays. a * 2 doubles every element; a + b adds them pairwise (as long as their agree; more on in Lesson 3).

Function	What it makes	Example
`np.array(list)`	an array from existing data	`np.array([1, 2, 3])` → `array([1, 2, 3])`
`np.zeros(shape)` / `np.ones(shape)`	filled with 0s / 1s	`np.zeros(3)` → `array([0., 0., 0.])`
`np.full(shape, value)`	filled with a constant	`np.full(3, 7)` → `array([7, 7, 7])`
`np.arange(start, stop, step)`	like `range()`, but returns an array	`np.arange(0, 10, 2)` → `array([0, 2, 4, 6, 8])`
`np.linspace(start, stop, n)`	`n` evenly spaced points, inclusive of both ends	`np.linspace(0, 1, 5)` → `array([0., 0.25, 0.5, 0.75, 1.])`
`np.eye(n)`	the n×n identity matrix	`np.eye(3)` → `[[1,0,0],[0,1,0],[0,0,1]]`

💡 Tip: shape is always a tuple, even for 1-D arrays: a 1-D array of 5 elements has shape (5,); note the trailing comma, not (5).

Now practice it

Exercises that use what this lesson just covered.

Import NumPyEasy NumPy VersionEasy Null Vector of Size 10Easy Memory Size of an ArrayEasy Range Vector 10–49Easy Identity MatrixEasy

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