# jax.numpy.array¶

jax.numpy.array(object, dtype=None, copy=True, order='K', ndmin=0)[source]

Create an array.

LAX-backend implementation of array(). Original docstring below.

array(object, dtype=None, copy=True, order=’K’, subok=False, ndmin=0)

Returns
outndarray

An array object satisfying the specified requirements.

empty_like : Return an empty array with shape and type of input. ones_like : Return an array of ones with shape and type of input. zeros_like : Return an array of zeros with shape and type of input. full_like : Return a new array with shape of input filled with value. empty : Return a new uninitialized array. ones : Return a new array setting values to one. zeros : Return a new array setting values to zero. full : Return a new array of given shape filled with value.

When order is ‘A’ and object is an array in neither ‘C’ nor ‘F’ order, and a copy is forced by a change in dtype, then the order of the result is not necessarily ‘C’ as expected. This is likely a bug.

>>> np.array([1, 2, 3])
array([1, 2, 3])


Upcasting:

>>> np.array([1, 2, 3.0])
array([ 1.,  2.,  3.])


More than one dimension:

>>> np.array([[1, 2], [3, 4]])
array([[1, 2],
[3, 4]])


Minimum dimensions 2:

>>> np.array([1, 2, 3], ndmin=2)
array([[1, 2, 3]])


Type provided:

>>> np.array([1, 2, 3], dtype=complex)
array([ 1.+0.j,  2.+0.j,  3.+0.j])


Data-type consisting of more than one element:

>>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')])
>>> x['a']
array([1, 3])


Creating an array from sub-classes:

>>> np.array(np.mat('1 2; 3 4'))
array([[1, 2],
[3, 4]])

>>> np.array(np.mat('1 2; 3 4'), subok=True)
matrix([[1, 2],
[3, 4]])