In Python NumPy what is a dimension and axis?

I am coding with Pythons NumPy module. If coordinates of a point in 3D space are described as [1, 2, 1], wouldn't that be three dimensions, three axis, a rank of three? Or if that is one dimension then shouldn't it be points (plural), not point?

Here is the documentation:

In Numpy dimensions are called axes. The number of axes is rank. For example, the coordinates of a point in 3D space [1, 2, 1] is an array of rank 1, because it has one axis. That axis has a length of 3.

Source: http://wiki.scipy.org/Tentative_NumPy_Tutorial

In numpy arrays, dimensionality refers to the number of axes needed to index it, not the dimensionality of any geometrical space. For example, you can describe the locations of points in 3D space with a 2D array:

    array([[0, 0, 0],
           [1, 2, 3],
           [2, 2, 2],
           [9, 9, 9]])

Which has shape of (4, 3) and dimension 2. But it can describe 3D space because the length of each row (axis 1) is three, so each row can be the x, y, and z component of a point's location. The length of axis 0 indicates the number of points (here, 4). However, that is more of an application to the math that the code is describing, not an attribute of the array itself. In mathematics, the dimension of a vector would be its length (e.g., x, y, and z components of a 3d vector), but in numpy, any "vector" is really just considered a 1d array of varying length. The array doesn't care what the dimension of the space (if any) being described is.

You can play around with this, and see the number of dimensions and shape of an array like so:

    In [262]: a = np.arange(9)

    In [263]: a
    Out[263]: array([0, 1, 2, 3, 4, 5, 6, 7, 8])

    In [264]: a.ndim    # number of dimensions
    Out[264]: 1

    In [265]: a.shape
    Out[265]: (9,)

    In [266]: b = np.array([[0,0,0],[1,2,3],[2,2,2],[9,9,9]])

    In [267]: b
    Out[267]: 
    array([[0, 0, 0],
           [1, 2, 3],
           [2, 2, 2],
           [9, 9, 9]])

    In [268]: b.ndim
    Out[268]: 2

    In [269]: b.shape
    Out[269]: (4, 3)

Arrays can have many dimensions, but they become hard to visualize above two or three:

    In [276]: c = np.random.rand(2,2,3,4)

    In [277]: c
    Out[277]: 
    array([[[[ 0.33018579,  0.98074944,  0.25744133,  0.62154557],
             [ 0.70959511,  0.01784769,  0.01955593,  0.30062579],
             [ 0.83634557,  0.94636324,  0.88823617,  0.8997527 ]],

            [[ 0.4020885 ,  0.94229555,  0.309992  ,  0.7237458 ],
             [ 0.45036185,  0.51943908,  0.23432001,  0.05226692],
             [ 0.03170345,  0.91317231,  0.11720796,  0.31895275]]],


           [[[ 0.47801989,  0.02922993,  0.12118226,  0.94488471],
             [ 0.65439109,  0.77199972,  0.67024853,  0.27761443],
             [ 0.31602327,  0.42678546,  0.98878701,  0.46164756]],

            [[ 0.31585844,  0.80167337,  0.17401188,  0.61161196],
             [ 0.74908902,  0.45300247,  0.68023488,  0.79672751],
             [ 0.23597218,  0.78416727,  0.56036792,  0.55973686]]]])

    In [278]: c.ndim
    Out[278]: 4

    In [279]: c.shape
    Out[279]: (2, 2, 3, 4)

From: stackoverflow.com/q/19389910

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