Initializing NumPy arrays and dtypes

There are several ways to initialize NumPy arrays besides np.arange:

1. Initialize an array of zeros with np.zeros. The np.zeros((5,2)) command creates a 5 x 2 array of zeros:

np.zeros((5,2))
array([[ 0.,  0.],
       [ 0.,  0.],
       [ 0.,  0.],
       [ 0.,  0.],
       [ 0.,  0.]])

1. Initialize an array of ones using np.ones. Introduce a dtype argument, set to np.int, to ensure that the ones are of NumPy integer type. Note that scikit-learn expects np.float arguments in arrays. The dtype refers to the type of every element in a NumPy array. It remains the same throughout the array. Every single element of the array below has a np.int integer type.

np.ones((5,2), dtype = np.int)
array([[1, 1],
       [1, 1],
       [1, 1],
       [1, 1],
       [1, 1]])

1. Use np.empty to allocate memory for an array of a specific size and dtype, but no particular initialized values:

np.empty((5,2), dtype = np.float)
array([[  3.14724935e-316,   3.14859499e-316],
       [  3.14858945e-316,   3.14861159e-316],
       [  3.14861435e-316,   3.14861712e-316],
       [  3.14861989e-316,   3.14862265e-316],
       [  3.14862542e-316,   3.14862819e-316]])

1. Use np.zeros, np.ones, and np.empty to allocate memory for NumPy arrays with different initial values.