NumPy is a popular Python library used for scientific computing. It provides a wide range of mathematical functions and tools for working with arrays. One of the functions provided by NumPy is random permutation.
Random permutation is a process of randomly shuffling the elements of an array. This is useful in many applications, such as shuffling a deck of cards or randomizing the order of a list of items. NumPy provides a function called random.permutation that can be used to generate a random permutation of an array.
Here is an example of how to use the random.permutation function:
<per>import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(np.random.permutation(arr))</per>
In this example, we first import the NumPy library and create an array called arr with the values 1 through 5. We then use the random.permutation function to generate a random permutation of the array. The output of this code will be a shuffled version of the original array.
The random.permutation function can also be used with multi-dimensional arrays. Here is an example:
<per>import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(np.random.permutation(arr))</per>
In this example, we create a 3x3 array called arr and use the random.permutation function to generate a random permutation of the array. The output of this code will be a shuffled version of the original array, with the rows and columns shuffled independently of each other.
NumPy also provides a function called shuffle that can be used to shuffle an array in place. Here is an example:
<per>import numpy as np
arr = np.array([1, 2, 3, 4, 5])
np.random.shuffle(arr)
print(arr)</per>
In this example, we create an array called arr and use the shuffle function to shuffle the elements of the array in place. The output of this code will be the shuffled version of the original array.
It is important to note that the shuffle function shuffles the array in place, meaning that the original array is modified. If you want to preserve the original array, you should make a copy of it before shuffling.
Overall, the NumPy random permutation functions provide a convenient way to shuffle arrays and generate random permutations. These functions are useful in many applications, such as data analysis, machine learning, and simulations.