How to generate random numbers in numpy

numpy's methods for generating random numbers are: 1. numpy.random.rand(); 2. numpy.random.randn(); 3. numpy.random.randint(); 4. numpy.random. random(); 5. numpy.random.seed().

The operating system for this tutorial: Windows 10 system, Python version 3.11.4, DELL G3 computer.

NumPy is a very powerful Python library for scientific computing and numerical calculations. It provides many functions to generate various types of random numbers. In this answer, I will introduce NumPy in detail Several common methods used to generate random numbers.

1. numpy.random.rand()

This method will generate an array of a given shape. The value of the array is within the interval [0, 1) Uniformly distributed random numbers in the shape of (0, 1). For example, np.random.rand(3, 2) A 3x2 array will be generated, the elements of which are random numbers in the range [0, 1).

import numpy as np
random_array = np.random.rand(3, 2)
print(random_array)

2. numpy.random.randn()

This function generates an array of a given shape. The values ​​of the array obey the standard normal distribution (the mean is 0, a random number with standard deviation 1). For example np.random.randn(3, 2) A 3x2 array will be generated, the elements of which are random numbers obeying the standard normal distribution.

import numpy as np
random_array = np.random.randn(3, 2)
print(random_array)

3. numpy.random.randint()

This function generates a random integer within the specified range. You can set the minimum and maximum values ​​of the range and the shape of the array. For example, np.randn.randint(1, 10, (3, 3)) A 3x3 array will be generated, with the elements in the array being random integers from 1 to 9.

import numpy as np
random_array = np.random.randint(1, 10, (3, 3))
print(random_array)

4. numpy.random.random()

This function will generate an array of a given shape. The value of the array is in the interval [0, 1) Uniformly distributed random numbers within. Similar to np.random.rand(), This function returns a vectorized version of the function of the random module of the Python standard library. For example, np.random.random((3, 3)) will generate a 3x3 An array of size where the elements are random numbers in the range [0, 1).

import numpy as np
random_array = np.random.random((3, 3))
print(random_array)

5, numpy.random.seed()

This function is used to specify the seed when generating pseudo-random numbers. Specifying the same seed will produce the same sequence of random numbers, which is very useful when debugging code. For example, np.random.seed(0) The seed will be set to 0 and the sequence of random numbers generated will be deterministic.

import numpy as np
np.random.seed(0)
random_array = np.random.rand(3, 3)
print(random_array)

These methods are just NumPy One of the many methods provided for generating random numbers. In practical applications, you may use different methods to generate random numbers that conform to a specific distribution or have specific properties. I hope these examples are helpful and give you a better understanding of how to Generate random numbers in NumPy.

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