In-depth analysis of numpy random number generation method

Detailed explanation of numpy random number generation method

numpy is an important scientific computing library for Python, which contains many functions for processing arrays and matrices. One of them is the random number generation function, which can easily generate various types of random numbers. This article will introduce numpy's random number generation method in detail and provide specific code examples.

1. numpy.random.rand()
   This function can generate a uniformly distributed random number of a specified size, with a value range between 0 and 1, including 0 but not including 1 . The following is a code example:

   import numpy as np
   
   # 生成一个2x3的均匀分布随机数矩阵
   arr = np.random.rand(2, 3)
   print(arr)

   Output result:

   [[0.34537909 0.43261776 0.78323571]
    [0.99208808 0.44671096 0.19721267]]

2. numpy.random.randn()
   This function can generate a standard normal distribution of a specified size Random numbers are normally distributed random numbers with a mean of 0 and a standard deviation of 1. The following is a code example:

   import numpy as np
   
   # 生成一个1x5的标准正态分布随机数数组
   arr = np.random.randn(1, 5)
   print(arr)

   Output result:

   [[-0.21346802 -0.65486627 -0.80993532  1.30767345 -0.51662313]]

3. numpy.random.randint()
   This function can generate an integer random number within the specified range , you can specify the upper and lower limits of the value range. Here is a code example:

   import numpy as np
   
   # 生成一个1x5的整数随机数数组，范围在0和10之间
   arr = np.random.randint(0, 10, size=(1, 5))
   print(arr)

   Output result:

   [[3 9 1 8 6]]

4. numpy.random.choice()
   This function can select from a given set of elements Randomly select one or more elements, you can specify the number of selections. The following is a code example:

   import numpy as np
   
   # 从列表中随机选择2个元素
   arr = np.random.choice(['apple', 'banana', 'orange', 'pear'], size=2)
   print(arr)

   Output result:

   ['banana' 'apple']

Through the above example, we can see that numpy provides many convenient random number generation methods, which can be based on Different needs generate different types of random numbers. These random number generation methods are widely used in scientific computing, machine learning, statistical analysis and other fields. Understanding and mastering these methods is of great benefit to data analysis and scientific computing programming work.

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