ndarray : N-Dimensional Array
Axes : Dimensions
Rank : Number of axes
Attributes of ndarray
ndarray.ndim : rank Eg: 2 for m x n matrix
ndarray.shape : tuple indicating size Eg: (m,n) for m x n matrix
ndarray.size : total num of elements Eg: mn for m x n matrix
Creating an ndarray
[1] Using arange
In : np.arange(6) Out: array([0, 1, 2, 3, 4, 5]) In : np.arange(0,7,2) Out: array([0, 2, 4, 6])
[2] Using arange, and then reshaping to the desired dimensions
In : np.arange(6).reshape(2,3)
Out:
array([[0, 1, 2],
[3, 4, 5]])
[3] By passing in a list
In : np.array([1,2,3]) Out: array([1, 2, 3])
[4] Using convenience functions
In : np.zeros((3,4))
Out:
array([[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]])
In : np.ones((3,4))
Out:
array([[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.]])
In : np.empty( (2,3) )
Out:
array([[ -2.68156382e+154, 1.53261208e-299, -2.68156382e+154],
[ 3.32459447e-309, -2.68156382e+154, 3.32605864e-309]])
[5] Create an ndarray with random numbers
In : np.random.random((3,3))
Out:
array([[ 0.05195457, 0.18754236, 0.73533617],
[ 0.07759585, 0.94097591, 0.16346386],
[ 0.68428612, 0.64856963, 0.78542591]])
