nums.numpy.array

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

Creates a BlockArray.

This docstring was copied from numpy.array.

Some inconsistencies with the NumS version may exist.

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'}, 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.

order	no copy	copy=True
’K’	unchanged	F & C order preserved, otherwise most similar order
’A’	unchanged	F order if input is F and not C, otherwise C order
’C’	C order	C order
’F’	F order	F order

When copy=False and a copy is made for other reasons, the result is the same as if copy=True, with some exceptions for A, see the Notes section. The default order is ‘K’.

subok (bool, optional) – If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default).
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

BlockArray

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

Only order=’K’ is supported.

Only ndmin=0 is currently supported.

subok must be False

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> nps.array([1, 2, 3]).get()  
array([1, 2, 3])

Upcasting:

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

More than one dimension:

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

Type provided:

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

nums.numpy.loadtxt

nums.numpy.empty