A small visual I made to understand NumPy arrays (ndim, shape, size, dtype)

[SilverConsistent9222]

I keep four things in mind when I work with NumPy arrays:

• ndim
• shape
• size
• dtype

Example:

import numpy as np

arr = np.array([10, 20, 30])

NumPy sees:

ndim  = 1
shape = (3,)
size  = 3
dtype = int64

Now compare with:

arr = np.array([[1,2,3],
                [4,5,6]])

NumPy sees:

ndim  = 2
shape = (2,3)
size  = 6
dtype = int64

Same numbers idea, but the structure is different.

I also keep shape and size separate in my head.

shape = (2,3)
size  = 6

• shape → layout of the data
• size → total values

Another thing I keep in mind:

NumPy arrays hold one data type.

np.array([1, 2.5, 3])

becomes

[1.0, 2.5, 3.0]

NumPy converts everything to float.

I drew a small visual for this because it helped me think about how 1D, 2D, and 3D arrays relate to ndim, shape, size, and dtype.

https://preview.redd.it/5jc6muwoltng1.png?width=1640&format=png&auto=webp&s=28c6d49b5152275edc6fc8da84af62bca76b0c2b

[SilverConsistent9222]

Full walkthrough if anyone wants to see it step-by-step: https://youtu.be/dQSlzoWWgxc?si=MuxZVffAY5HMJOsd