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bundles / numpy latest / numpy / ndarray

GenericAlias

numpy:ndarray

source: /opt/hostedtoolcache/Python/3.14.5/x64/lib/python3.14/types.py

Signature

def   ndarray ( shape ,  dtype = <class 'float'> ,  buffer = None ,  offset = 0 ,  strides = None ,  order = None )

Summary

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.)

Extended Summary

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. Default is numpy.float64.

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.

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.

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')
✗
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
✓

See also

array

Construct an array.

dtype

Create a data-type.

empty

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

numpy.typing.NDArray

An ndarray alias generic <generic type> w.r.t. its dtype.type <numpy.dtype.type>.

zeros

Create an array, each element of which is zero.

Aliases

  • numpy.ndarray
  • numpy.typing.NDArray

Referenced by

This package

Other packages