numpy.polynomial.polynomial.polyzero

polynomial.polynomial. polyzero = array([0])

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.