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, 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’, ‘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=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.  
likearray_like


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.    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 >>> 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]])