Numpy 简明教程

NumPy - Ndarray Object

NumPy 中定义的最重要的对象是一个 N 维数组类型，称为 ndarray 。它描述同类型的项的集合。可以使用基于零的索引访问集合中的项。

The most important object defined in NumPy is an N-dimensional array type called ndarray. It describes the collection of items of the same type. Items in the collection can be accessed using a zero-based index.

ndarray 中的每一项在内存中占用相同大小的块。ndarray 中的每个元素都是数据类型对象（称为 dtype ）。

Every item in an ndarray takes the same size of block in the memory. Each element in ndarray is an object of data-type object (called dtype).

从 ndarray 对象（通过切片）提取的任何项都通过一种数组标量类型表示为一个 Python 对象。下图展示了 ndarray、数据类型对象 (dtype) 与数组标量类型之间的关系：

Any item extracted from ndarray object (by slicing) is represented by a Python object of one of array scalar types. The following diagram shows a relationship between ndarray, data type object (dtype) and array scalar type −

ndarray

在之后教程中所述的不同数组创建例程中，可以构造 ndarray 类的实例。使用 NumPy 中的数组函数按如下方式创建基本 ndarray：

An instance of ndarray class can be constructed by different array creation routines described later in the tutorial. The basic ndarray is created using an array function in NumPy as follows −

numpy.array

它通过公开数组接口的任意对象或通过返回数组的任意方法创建 ndarray。

It creates an ndarray from any object exposing array interface, or from any method that returns an array.

numpy.array(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0)

上述构造函数接受以下参数：

The above constructor takes the following parameters −

Sr.No.

Parameter & Description

1

object Any object exposing the array interface method returns an array, or any (nested) sequence.

2

dtype Desired data type of array, optional

3

copy Optional. By default (true), the object is copied

4

order C (row major) or F (column major) or A (any) (default)

5

subok By default, returned array forced to be a base class array. If true, sub-classes passed through

6

ndmin Specifies minimum dimensions of resultant array

仔细查看以下示例，以便更好地理解。

Take a look at the following examples to understand better.

Example 1

import numpy as np
a = np.array([1,2,3])
print a

输出如下 −

The output is as follows −

[1, 2, 3]

Example 2

# more than one dimensions
import numpy as np
a = np.array([[1, 2], [3, 4]])
print a

输出如下 −

The output is as follows −

[[1, 2]
 [3, 4]]

Example 3

# minimum dimensions
import numpy as np
a = np.array([1, 2, 3,4,5], ndmin = 2)
print a

输出如下 −

The output is as follows −

[[1, 2, 3, 4, 5]]

Example 4

# dtype parameter
import numpy as np
a = np.array([1, 2, 3], dtype = complex)
print a

输出如下 −

The output is as follows −

[ 1.+0.j,  2.+0.j,  3.+0.j]

ndarray 对象由连续的一维计算机内存段组成，该段与一个索引方案结合在一起，将每个项目映射到内存块中的一个位置。内存块按行优先顺序 (C 样式) 或列优先顺序 (FORTRAN 或 MatLab 样式) 保存元素。

The ndarray object consists of contiguous one-dimensional segment of computer memory, combined with an indexing scheme that maps each item to a location in the memory block. The memory block holds the elements in a row-major order (C style) or a column-major order (FORTRAN or MatLab style).