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numpy.ndarray() ⚬｜Documentation｜1st｜20251021001533-00-⌔

numpy.ndarray — NumPy v2.1 Manual#numpy.ndarray

class numpy.ndarray(shape, dtype=float, buffer=None, offset=0, strides=None, order=None)

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__:

• 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:

>>> import numpy as np
>>> 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

View of the transposed array.

data: buffer

Python buffer object pointing to the start of the array’s data.

dtype: dtype object

Data-type of the array’s elements.

flags: dict

Information about the memory layout of the array.

flat: numpy.flatiter object

A 1-D iterator over the array.

imag: ndarray

The imaginary part of the array.

real: ndarray

The real part of the array.

size: int

Number of elements in the array.

itemsize: int

Length of one array element in bytes.

nbytes: int

Total bytes consumed by the elements of the array.

ndim: int

Number of array dimensions.

shape: tuple of ints

Tuple of array dimensions.

strides: tuple of ints

Tuple of bytes to step in each dimension when traversing an array.

ctypes: ctypes object

An object to simplify the interaction of the array with the ctypes module.

base: ndarray

Base object if memory is from some other object.

Methods:

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