numpy.polynomial.legendre.legx   polynomial.legendre.legx = array([0, 1])
 
An array object represents a multidimensional, homogeneous array of fixed-size items. An associated data-type object describes the format of each element in the array (its byte-order, how many bytes it occupies in memory, whether it is an integer, a floating point number, or something else, etc.) Arrays should be constructed using array, zeros or empty (refer to the See Also section below). The parameters given here refer to a low-level method (ndarray(…)) for instantiating an array. For more information, refer to the numpy module and examine the methods and attributes of an array.  Parameters 
 (for the __new__ method; see Notes below)

shapetuple of ints


Shape of created array.  
dtypedata-type, optional


Any object that can be interpreted as a numpy data type.  
bufferobject exposing buffer interface, optional


Used to fill the array with data.  
offsetint, optional


Offset of array data in buffer.  
stridestuple of ints, optional


Strides of data in memory.  
order{‘C’, ‘F’}, optional


Row-major (C-style) or column-major (Fortran-style) order.      See also  array

Construct an array.  zeros

Create an array, each element of which is zero.  empty

Create an array, but leave its allocated memory unchanged (i.e., it contains “garbage”).  dtype

Create a data-type.  numpy.typing.NDArray

An ndarray alias generic w.r.t. its dtype.type.    Notes There are two modes of creating an array using __new__:  If buffer is None, then only shape, dtype, and order are used. If buffer is an object exposing the buffer interface, then all keywords are interpreted.  No __init__ method is needed because the array is fully initialized after the __new__ method. Examples These examples illustrate the low-level ndarray constructor. Refer to the See Also section above for easier ways of constructing an ndarray. First mode, buffer is None: >>> np.ndarray(shape=(2,2), dtype=float, order='F')
array([[0.0e+000, 0.0e+000], # random
       [     nan, 2.5e-323]])
 Second mode: >>> np.ndarray((2,), buffer=np.array([1,2,3]),
...            offset=np.int_().itemsize,
...            dtype=int) # offset = 1*itemsize, i.e. skip first element
array([2, 3])
  Attributes 
 
Tndarray


Transpose of the array.  
databuffer


The array’s elements, in memory.  
dtypedtype object


Describes the format of the elements in the array.  
flagsdict


Dictionary containing information related to memory use, e.g., ‘C_CONTIGUOUS’, ‘OWNDATA’, ‘WRITEABLE’, etc.  
flatnumpy.flatiter object


Flattened version of the array as an iterator. The iterator allows assignments, e.g., x.flat = 3 (See ndarray.flat for assignment examples; TODO).  
imagndarray


Imaginary part of the array.  
realndarray


Real part of the array.  
sizeint


Number of elements in the array.  
itemsizeint


The memory use of each array element in bytes.  
nbytesint


The total number of bytes required to store the array data, i.e., itemsize * size.  
ndimint


The array’s number of dimensions.  
shapetuple of ints


Shape of the array.  
stridestuple of ints


The step-size required to move from one element to the next in memory. For example, a contiguous (3, 4) array of type int16 in C-order has strides (8, 2). This implies that to move from element to element in memory requires jumps of 2 bytes. To move from row-to-row, one needs to jump 8 bytes at a time (2 * 4).  
ctypesctypes object


Class containing properties of the array needed for interaction with ctypes.  
basendarray


If the array is a view into another array, that array is its base (unless that array is also a view). The base array is where the array data is actually stored.