An easy-to-understand guide to viewing numpy versions

NumPy is an important scientific computing package in Python. It provides many mathematics-related functions and is widely used in fields such as data analysis, machine learning, and deep learning. In NumPy, array is the main data structure, and array operations are one of the core functions of NumPy.

This article will introduce the basic operations and viewing methods of NumPy arrays, allowing readers to understand how to access the elements of the array, modify the shape of the array, view the properties of the array, etc.

1. Creating an array

In NumPy, you can use the numpy.array() function to create an array, as shown below:

import numpy as np
arr = np.array([1, 2, 3, 4, 5])

At this time, arr is a one-dimensional array containing 5 elements. We can also create one-dimensional arrays through the numpy.arange() function or numpy.linspace() function:

arr1 = np.arange(10)   # 生成一个0到9的一维数组
arr2 = np.linspace(0, 10, 11)   # 生成一个0到10之间，含11个元素的一维数组

2. Accessing elements

Accessing elements in NumPy arrays This can be achieved through array subscripts. Note that array subscripts start from 0. For multidimensional arrays, you can use multiple subscripts to access specific elements. For example:

arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(arr[0, 0])   # 访问第一个元素 1
print(arr[1, 2])   # 访问第二行第三列的元素 6

3. Modify the shape

In NumPy, we can use the numpy.reshape() function to modify the shape of the array. For example:

arr = np.array([1, 2, 3, 4, 5, 6])
new_arr = arr.reshape(2, 3)   # 将一维数组变为二维数组，形状为(2,3)

At this time, the shape of new_arr is (2,3), that is, a matrix with two rows and three columns, and the elements are:

1  2  3
4  5  6

4. View array attributes

In NumPy, we can view the shape, number of elements, data type and other properties of the array. For example:

arr = np.array([1, 2, 3, 4, 5, 6])
print(arr.shape)   # 输出形状 (6,)
print(arr.size)   # 输出元素个数 6
print(arr.dtype)   # 输出数据类型 int32

Among them, shape represents the shape of the array, size represents the number of array elements, and dtype represents the data type of the array.

5. Other array operations

(1) To perform slicing operations on arrays, you can use the ":" operator. For example:

arr = np.array([1, 2, 3, 4, 5, 6])
print(arr[1:4])   # 输出[2 3 4]

(2) Perform some statistical operations on the array, such as calculating the sum, average, standard deviation, etc. of the elements in the array. For example:

arr = np.array([1, 2, 3, 4, 5, 6])
print(np.sum(arr))   # 计算元素的和，输出21
print(np.mean(arr))   # 计算平均值，输出3.5
print(np.std(arr))   # 计算标准差，输出1.707825127659933

(3) Perform some logical operations on the array, such as filtering out elements in the array that meet the conditions. For example:

arr = np.array([1, 2, 3, 4, 5, 6])
print(arr[arr > 3])   # 输出[4 5 6]

The above are the basic methods of using NumPy to operate arrays. We can use these methods to access and modify the shape and elements of the array, as well as perform some statistical and logical operations.

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