numpy.array#

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

Create an array.

Parameters:

objectarray_like

An array, any object exposing the array interface, an object whose__array__ method returns an array, or any (nested) sequence.If object is a scalar, a 0-dimensional array containing object isreturned.

dtypedata-type, optional

The desired data-type for the array. If not given, NumPy will try to usea defaultdtype that can represent the values (by applying promotionrules when necessary.)

copybool, optional

IfTrue (default), then the array data is copied. IfNone,a copy will only be made if__array__ returns a copy, if obj isa nested sequence, or if a copy is needed to satisfy any of the otherrequirements (dtype,order, etc.). Note that any copy ofthe data is shallow, i.e., for arrays with object dtype, the newarray will point to the same objects. See Examples forndarray.copy.ForFalse it raises aValueError if a copy cannot be avoided.Default:True.

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=None and a copy is made for other reasons, the result isthe same as ifcopy=True, with some exceptions for ‘A’, see theNotes section. The default order is ‘K’.

subokbool, optional

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

ndminint, optional

Specifies the minimum number of dimensions that the resultingarray should have. Ones will be prepended to the shape asneeded to meet this requirement.

likearray_like, optional

Reference object to allow the creation of arrays which are notNumPy arrays. If an array-like passed in aslike supportsthe__array_function__ protocol, the result will be definedby it. In this case, it ensures the creation of an array objectcompatible with that passed in via this argument.

New in version 1.20.0.

Returns:

outndarray

An array object satisfying the specified requirements.

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.

copy

Return an array copy of the given object.

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

Try it in your browser!

>>>importnumpyasnp>>>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], dtype=int32)

Creating an array from sub-classes:

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

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

Go BackOpen In Tab