Numpy has built-in functions for creating arrays.

>>> np.zeros((2, 3))

array([[ 0., 0., 0.],

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

**zeros(shape)**will create an array filled with "0" values with the specified shape. The default dtype is float64.**Example:**>>> np.zeros((2, 3))

array([[ 0., 0., 0.],

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

**ones(shape)**will create an array filled with 1 values.

**Example:**

>>> np.ones((2,3))

array([[ 1., 1., 1.],

[ 1., 1., 1.]])

**arange()**will create arrays with regular increment values

**Example:**

>>> np.arange(10)

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

>>> np.arange(2, 10, dtype=np.float)

array([ 2., 3., 4., 5., 6., 7., 8., 9.])

>>> np.arange(2, 3, 0.1)

array([ 2. , 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9])

**linspace()**will create arrays with a specified number of elements, and spaced equally between the specified beginning and end values.

**Example**:

>>> np.linspace(1., 4., 6)

array([ 1. , 1.6, 2.2, 2.8, 3.4, 4. ])

**indices()**will create a set of arrays (stacked as a one-higher dimensioned array), one per dimension with each representing variation in that dimension.

**Example**:

>>> np.indices((3,3))

array([[[0, 0, 0],

[1, 1, 1],

[2, 2, 2]],

[[0, 1, 2],

[0, 1, 2],

[0, 1, 2]]])