nums.numpy.array

nums.numpy.array(object, dtype=None, copy=True, order='K', ndmin=0, subok=False) → nums.core.array.blockarray.BlockArray

Creates a BlockArray.

This docstring was copied from numpy.array.

Some inconsistencies with the NumS version may exist.

objectarray_like

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

dtypedata-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.

copybool, 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’.

subokbool, optional

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

ndminint, 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.

outBlockArray

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.

Only order=’K’ is supported.

Only ndmin=0 is currently supported.

subok must be False

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])