numpy.random.random() is one of the function for doing random sampling in numpy. It returns an array of specified shape and fills it with random floats in the half-open interval [0.0, 1.0).
Parameters : size : [int or tuple of ints, optional] Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned.
Return : Array of random floats in the interval [0.0, 1.0). or a single such random float if size not provided.
Random numbers are the numbers that return a random integer. The random number does not mean a different number every time, but it means something that cannot be predicted logically. We might get the same number two times while creating a random number. In Python, there are different libraries that can help us to create random numbers. For example, by using a random module or Numpy module, we can create different kinds of random numbers. In this tutorial, we will learn about creating random numbers in the NumPy module.
Getting started with Numpy random numbers in Python
A random number is a number generated using a large set of numbers and a mathematical algorithm that gives equal probability to all numbers occurring in the specified distribution. Random numbers are most commonly produced with the help of a random number generator. Random numbers have important applications, especially in cryptography where they act as ingredients in encryption keys. In this tutorial, we learned about how to create random numbers in the NumPy module. The random method in NumPy helps us to create random numbers. We covered different ways to create randoms numbers along with various examples. Moreover, we also discussed how we create a random valued NumPy array and also how to generate random values from an Array. All in all, this tutorial covers all the methods and various examples to make you understand random numbers in NumPy.
NumPy random.rand() function in Python is used to return random values from a uniform distribution in a specified shape. This function creates an array of the given shape and it fills with random samples from the uniform distribution. This function takes a tuple, to specify the size of an array, which behavior same as the other NumPy functions like the numpy.ones() function and numpy.zeros() function.
For more NumPy examples refer to NumPy Tutorial. In this article, I will explain how to use the NumPy random.rand() function syntax and how it is used to create an array with random values based on a given shape with examples.
1. Quick Examples of random.rand() Function
If you are in a hurry, below are some quick examples of how to use the Python NumPy random.rand() function.
# Below are the quick examples
# Example 1: Use numpy.random.rand() function
arr = np.random.rand()
# Example 2: Use numpy.random.seed() function
np.random.seed(0)
arr = np.random.rand()
# Example 3: Create 1-dimensional array with random values
arr = np.random.rand(6)
# Example 4: Get 2-D Randomly array
arr = np.random.rand(2,5)
# Example 5: Generate 3-dimensional arrays with random values
arr = np.random.rand(5,2,4)
2 Syntax of random.rand()
Following is the syntax of the numpy.random.rand() function.
# Syntax of NumPy random.rand()
random.rand(d0, d1, ..., dn)
2.1 Parameters of random.rand()
Following are the parameters of random.rand() function.
d0, d1, …, dn – The dimension of the returned array and it must be int type. If no argument is specified a single Python float is returned.
2.2 Return Value
It returns a random array of specified shapes, filled with random values of float type from a uniform distribution over [0,1].
3. Usage of NumPy random.rand()
The random.rand() is a numpy library function that returns an array of random samples from the
# Syntax of NumPy random.rand()
random.rand(d0, d1, ..., dn)
1. It allows dimensions as an argument and returns an array of specified dimensions. If we don’t provide any argument, it will return the float value.
This function returns the random number without passing any parameter. You might get different random numbers when you run the same code multiple times. Let’s take an example,
import numpy as np
# Use random.rand() function
arr = np.random.rand()
print(arr)
# Output
# 0.7507775570160498
Alternatively use the
# Syntax of NumPy random.rand()
random.rand(d0, d1, ..., dn)
2 function avoid the above problem. It will return the same result with every execution by setting the seed() value.
# Use numpy.random.seed() function
np.random.seed(0)
arr = np.random.rand()
print(arr)
# Output
# 0.5488135039273248
4. Get 1-D NumPy Array of Random Values
Pass the shape of the array as an argument into random.rand()function to create a one-dimensional NumPy array of random values. This function will return an array of a given dimension. The below example returns 6 random values with shape 1,6 (1 row and 6 columns).
# Get 1-dimensional array of random values
arr = np.random.rand(6)
print(arr)
# Output
# [0.56804456 0.92559664 0.07103606 0.0871293 0.0202184 0.83261985]
5. Get 2-D NumPy Array of Random Values
To get random values of two-dimensional arrays, pass (tuple of ints) the shape of the array with value 2 or more for rows. It returns the two-dimensional array of specified shape.
# Get 2-Dimensional array of random values
arr = np.random.rand(2,5)
print(arr)
# Output
# [[0.77815675 0.87001215 0.97861834 0.79915856 0.46147936]
# [0.78052918 0.11827443 0.63992102 0.14335329 0.94466892]]
6. Get 3-D NumPy Array of Random Values
Let’s generate a three-dimensional random array with a specified shape. Similarly, you can also generate any random arrays of any size using the
# Syntax of NumPy random.rand()
random.rand(d0, d1, ..., dn)
4 function.
# Generate 3-dimensional array of random values
arr = np.random.rand(5,2,4)
print(arr)
