numpy.array¶

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

Create an array.

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 willbe determined as the minimum type required to hold the objects in thesequence. This argument can only be used to ‘upcast’ the array. Fordowncasting, use the .astype(t) method. copy : bool, optional If true (default), then the object is copied. Otherwise, a copy willonly 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, thenewly created array will be in C order (row major) unless ‘F’ isspecified, 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 Whencopy=False and a copy is made for other reasons, the result isthe same as ifcopy=True, with some exceptions forA, see theNotes section. The default order is ‘K’. subok : bool, optional If True, then sub-classes will be passed-through, otherwisethe returned array will be forced to be a base-class array (default). ndmin : int, optional Specifies the minimum number of dimensions that the resultingarray should have. Ones will be pre-pended to the shape asneeded to meet this requirement.

Returns:

out : ndarray An array object satisfying the specified requirements.

See also

empty,empty_like,zeros,zeros_like,ones,ones_like,full,full_like

Notes

When order is ‘A’ andobject 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 isnot 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]])