# 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)
    shape tuple of ints

      Shape of created array.

    dtype data-type, optional

      Any object that can be interpreted as a numpy data type.

    buffer object exposing buffer interface, optional

      Used to fill the array with data.

    offset int, optional

      Offset of array data in buffer.

    strides tuple 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__`:

  1. If buffer is None, then only `shape`, `dtype`, and order are used.

  2. 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:
    T ndarray

      Transpose of the array.

    data buffer

      The array’s elements, in memory.

    dtype dtype object

      Describes the format of the elements in the array.

    flags dict

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

    flat numpy.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).

    imag ndarray

      Imaginary part of the array.

    real ndarray

      Real part of the array.

    size int

      Number of elements in the array.

    itemsize int

      The memory use of each array element in bytes.

    nbytes int

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

    ndim int

      The array’s number of dimensions.

    shape tuple of ints

      Shape of the array.

    strides tuple 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`).

    ctypes ctypes object

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

    base ndarray

      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.