How to generate random numbers using numpy

numpy is a very commonly used scientific computing library in Python. It provides many fast and efficient numerical operations and data processing functions. In numpy, we can easily generate random numbers. This article will introduce the method of generating random numbers in numpy and give specific code examples.

The functions that generate random numbers in numpy mainly include the rand() function, randn() function, randint() function, uniform() function, normal() function, etc. under the random module.

1. rand() function: This function is used to generate uniformly distributed random numbers between [0,1). We can specify the shape of the generated random numbers, such as generating a one-dimensional array or a two-dimensional array, etc.

The sample code is as follows:

import numpy as np

#生成一个具有5个元素的一维数组
arr1 = np.random.rand(5)
print(arr1)

#生成一个2行3列的二维数组
arr2 = np.random.rand(2, 3)
print(arr2)

2. randn() function: This function is used to generate random numbers from the standard normal distribution (mean 0, standard deviation 1) . Likewise, we can specify the shape of the generated random numbers.

The sample code is as follows:

import numpy as np

#生成一个具有5个元素的一维数组
arr1 = np.random.randn(5)
print(arr1)

#生成一个2行3列的二维数组
arr2 = np.random.randn(2, 3)
print(arr2)

3. randint() function: This function is used to generate random integers within the specified range. We need to specify the lower and upper bounds for generating random integers, as well as the shape of the generated random numbers.

The sample code is as follows:

import numpy as np

#生成一个在[0,10)之间的一维整数数组
arr1 = np.random.randint(0, 10, size=5)
print(arr1)

#生成一个在[0,10)之间2行3列的二维整数数组
arr2 = np.random.randint(0, 10, size=(2, 3))
print(arr2)

4. uniform() function: This function is used to generate uniformly distributed random numbers within a specified range. We need to specify the lower bound, upper bound and shape of the generated random numbers.

The sample code is as follows:

import numpy as np

#生成一个在[2,5)之间的一维数组
arr1 = np.random.uniform(2, 5, size=5)
print(arr1)

#生成一个在[2,5)之间2行3列的二维数组
arr2 = np.random.uniform(2, 5, size=(2, 3))
print(arr2)

5. normal() function: This function is used to generate random numbers from a normal distribution with a specified mean and standard deviation. We need to specify the mean, standard deviation and shape of the generated random numbers.

The sample code is as follows:

import numpy as np

#生成均值为2，标准差为0.5的一维数组
arr1 = np.random.normal(2, 0.5, size=5)
print(arr1)

#生成均值为2，标准差为0.5的2行3列的二维数组
arr2 = np.random.normal(2, 0.5, size=(2, 3))
print(arr2)

Through the above code examples, we can see that numpy provides a wealth of random number generation functions, which can meet various needs for generating random numbers, and Very easy to use. In practical applications, we can choose an appropriate random number generation function according to specific needs, and generate random numbers that meet our needs by specifying parameters.

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