We learned about how to create an array using np.array(). NumPy also provides a few other methods to create new arrays.

Functions np.zeros() and np.ones()

The functions zeros and ones create new arrays of specified dimensions filled with these values (Os and 1s). These are perhaps the most commonly used functions to create new arrays:

>>> import numpy as np
>>> np.zeros(6)
array([ 0.,  0.,  0.,  0.,  0.,  0.])
>>> np.zeros([2,3])
array([[ 0.,  0.,  0.],
       [ 0.,  0.,  0.]])
>>> np.ones([3,3])
array([[ 1.,  1.,  1.],
       [ 1.,  1.,  1.],
       [ 1.,  1.,  1.]])
>>>

Function zeros_like() and ones_like()

The zeros_like() and ones_like() functions create a new array with the same dimensions and type of an existing one:

>>> a = np.array([[1, 2, 3], [4, 5, 6]], float)
>>> np.zeros_like(a)
array([[ 0.,  0.,  0.],
       [ 0.,  0.,  0.]])
>>> np.ones_like(a)
array([[ 1.,  1.,  1.],
       [ 1.,  1.,  1.]])
>>>

Function np.arrange()

The arrange function is similar to the range function but returns an array. It returns evenly spaced values within a given interval.