Beyond the Basic Shuffle: Achieving Orderly Rearrangement of Corresponding Elements in NumPy Arrays

numpy.random.permutation:

This function from NumPy's random module generates a random permutation of integers. It creates a new array containing a random rearrangement of indices from 0 to the length of the array minus one.

Combining and Shuffling:

• We can concatenate the two arrays we want to shuffle using np.c_. This creates a single array where each row combines corresponding elements from the original arrays.
• Apply np.random.shuffle to the combined array. This shuffles the order of rows, effectively shuffling elements from both arrays together.
• Separate the shuffled rows back into two individual arrays based on their original shapes.

shuffle_in_unison function:

Here's a reusable function shuffle_in_unison that encapsulates this process:

import numpy as np

def shuffle_in_unison(a, b):
  """
  Shuffles two numpy arrays in unison.

  Args:
      a: A numpy array.
      b: A numpy array with the same length as a.

  Returns:
      A tuple containing the shuffled arrays.
  """
  assert a.shape[0] == b.shape[0]
  permutation = np.random.permutation(a.shape[0])
  return a[permutation], b[permutation]

This function first checks if both arrays have the same length. Then, it generates a random permutation using np.random.permutation. Finally, it indexes both arrays using the same permutation to achieve shuffling in unison and returns the shuffled arrays.

Example Usage:

arr1 = np.array([1, 2, 3, 4, 5])
arr2 = np.array(['a', 'b', 'c', 'd', 'e'])

shuffled_arr1, shuffled_arr2 = shuffle_in_unison(arr1, arr2)

print(f"Original arrays:\narr1: {arr1}\narr2: {arr2}")
print(f"\nShuffled arrays:\nshuffled_arr1: {shuffled_arr1}\nshuffled_arr2: {shuffled_arr2}")

This code snippet demonstrates how to use the shuffle_in_unison function. It shuffles two arrays containing numbers and letters while maintaining the correspondence between elements.

This approach offers an efficient way to shuffle multiple NumPy arrays together, ensuring elements from corresponding positions are shuffled identically.

import numpy as np

def shuffle_in_unison(a, b):
  """
  Shuffles two numpy arrays in unison.

  Args:
      a: A numpy array.
      b: A numpy array with the same length as a.

  Returns:
      A tuple containing the shuffled arrays.
  """
  assert a.shape[0] == b.shape[0]
  permutation = np.random.permutation(a.shape[0])
  return a[permutation], b[permutation]

# Example Usage
arr1 = np.array([1, 2, 3, 4, 5])
arr2 = np.array(['a', 'b', 'c', 'd', 'e'])

shuffled_arr1, shuffled_arr2 = shuffle_in_unison(arr1, arr2)

print(f"Original arrays:\narr1: {arr1}\narr2: {arr2}")
print(f"\nShuffled arrays:\nshuffled_arr1: {shuffled_arr1}\nshuffled_arr2: {shuffled_arr2}")

This code defines the shuffle_in_unison function and then demonstrates how to use it. It shuffles two arrays, arr1 containing numbers and arr2 containing letters, and prints the original and shuffled versions. The shuffled arrays will maintain the correspondence between elements (e.g., the element at the same index in both arrays will be shuffled together).

List Comprehension with random.shuffle:

This approach first creates a list of tuples containing corresponding elements from both arrays. Then, it shuffles the list using random.shuffle from the random module and unpacks the shuffled list back into separate arrays.

import numpy as np
import random

def shuffle_in_unison_list(a, b):
  """
  Shuffles two numpy arrays in unison using list comprehension.

  Args:
      a: A numpy array.
      b: A numpy array with the same length as a.

  Returns:
      A tuple containing the shuffled arrays.
  """
  assert a.shape[0] == b.shape[0]
  data = list(zip(a, b))
  random.shuffle(data)
  shuffled_a, shuffled_b = zip(*data)
  return np.array(shuffled_a), np.array(shuffled_b)

np.random.choice with Resampling:

This method utilizes np.random.choice to sample indices with replacement from an array containing 0 to the length of the arrays minus one. This generates a random permutation. Then, it uses this permutation to index both arrays for shuffling.

import numpy as np

def shuffle_in_unison_choice(a, b):
  """
  Shuffles two numpy arrays in unison using np.random.choice

  Args:
      a: A numpy array.
      b: A numpy array with the same length as a.

  Returns:
      A tuple containing the shuffled arrays.
  """
  assert a.shape[0] == b.shape[0]
  permutation = np.random.choice(a.shape[0], size=a.shape[0], replace=True)
  return a[permutation], b[permutation]

Both methods achieve shuffling in unison, but they have slight differences:

• List comprehension: This method might be less efficient for large arrays due to the creation of a temporary list.
• np.random.choice: This method avoids creating an intermediate list and might be more memory efficient for larger arrays.

Choose the method that best suits your specific needs and the size of your data.