jax.numpy.array

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

Create an array.

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

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

Parameters

• object (array_like) – An array, any object exposing the array interface, an object whose __array__ method returns an array, or any (nested) sequence.

• dtype (data-type, optional) – The desired data-type for the array. If not given, then the type will be determined as the minimum type required to hold the objects in the sequence.

• copy (bool, optional) – If true (default), then the object is copied. Otherwise, a copy will only be made if __array__ returns a copy, if obj is a nested sequence, or if a copy is needed to satisfy any of the other requirements (dtype, order, etc.).

• order ({'K', 'A', 'C', 'F'}, optional) – Specify the memory layout of the array. If object is not an array, the newly created array will be in C order (row major) unless ‘F’ is specified, in which case it will be in Fortran order (column major). If object is an array the following holds.

• ndmin (int, optional) – Specifies the minimum number of dimensions that the resulting array should have. Ones will be pre-pended to the shape as needed to meet this requirement.

Returns

out – An array object satisfying the specified requirements.

Return type

ndarray

See also

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.

Notes

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.

Examples

>>> 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]])