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Dimension

I'm new to Numpy and trying to understand the basic question of what dimensionality is,

I tried the following commands and tried to understand why ndim for the last 2 arrays are the same?

>>> a= array([1,2,3])
>>> a.ndim
1
>>> a= array([[1,2,3],[4,5,6]])
>>> a
array([[1, 2, 3],
       [4, 5, 6]])
>>> a.ndim
2
>>> a=arange(15).reshape(3,5)
>>> a.ndim
2

>>> a
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14]])

My understanding ..

Case 1:
    array([[1, 2, 3],
           [4, 5, 6]])

2 elements are present in main lists, so ndim is-2

Case 2:
    array([[ 0,  1,  2,  3,  4],
           [ 5,  6,  7,  8,  9],
           [10, 11, 12, 13, 14]])

There are 3 elements in the main lists, do ndim is-3

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1 answer

shapeAn array is a tuple of its size. An array with one dimension has the form (n,). A two-dimensional array has the form (n, m) (for example, your case 2 and 3), and a three-dimensional array has the form (n, m, k), etc.

Therefore, although the form of your second and third examples is different, form No. in both cases, the size is two:

>>> a= np.array([[1,2,3],[4,5,6]])
>>> a.shape
(2, 3)

>>> b=np.arange(15).reshape(3,5)
>>> b.shape
(3, 5)

If you want to add another dimension to your examples, you will need to do something like this:

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

or

np.arange(15).reshape(3,5,1)

You can add measurements this way:

One dimension:

>>> a = np.zeros((2))
array([ 0.,  0.])
>>> a.shape
(2,)
>>> a.ndim
1

Two dimensions:

>>> b = np.zeros((2,2))
array([[ 0.,  0.],
       [ 0.,  0.]])
>>> b.shape
(2,2)
>>> b.ndim
2

Three dimensions:

>>> c = np.zeros((2,2,2))
array([[[ 0.,  0.],
        [ 0.,  0.]],

       [[ 0.,  0.],
        [ 0.,  0.]]])
>>> c.shape
(2,2,2)
>>> c.ndim
3

Four dimensions:

>>> d = np.zeros((2,2,2,2))
array([[[[ 0.,  0.],
         [ 0.,  0.]],

        [[ 0.,  0.],
         [ 0.,  0.]]],


       [[[ 0.,  0.],
         [ 0.,  0.]],

        [[ 0.,  0.],
         [ 0.,  0.]]]])
>>> d.shape
(2,2,2,2)
>>> d.ndim
4
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