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 is returned.

dtypedata-type, optional

The desired data-type for the array. If not given, NumPy will try to use a default dtype that can represent the values (by applying promotion rules when necessary.)

copybool, optional

If True (default), then the array data is copied. If None, 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.). Note that any copy of the data is shallow, i.e., for arrays with object dtype, the new array will point to the same objects. See Examples for ndarray.copy. For False it raises a ValueError 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, 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=None 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 prepended to the shape as needed to meet this requirement.

likearray_like, optional

Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as like supports the __array_function__ protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible 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’ 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

>>> import numpy as np
>>> 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]])