Random Permutations¶

NumPy provides functions for randomly reordering array elements.

np.random.shuffle¶

Shuffles an array in-place, modifying the original.

1. Returns None¶

import numpy as np

def main():
    x = [1, 4, 9, 12, 15]
    result = np.random.shuffle(x)
    print(result)  # None

if __name__ == "__main__":
    main()

The function modifies the array in-place and returns None.

2. List Shuffle¶

import numpy as np

def main():
    x = [1, 4, 9, 12, 15]
    np.random.shuffle(x)
    print(x)  # Shuffled list

if __name__ == "__main__":
    main()

3. Array Shuffle¶

import numpy as np

def main():
    x = np.array([1, 4, 9, 12, 15])
    np.random.shuffle(x)
    print(x)

if __name__ == "__main__":
    main()

2D Array Shuffle¶

For multi-dimensional arrays, shuffle operates along the first axis.

1. Row Shuffling¶

import numpy as np

def main():
    x = np.arange(9).reshape((3, 3))
    print("Before shuffle:")
    print(x)

    np.random.shuffle(x)
    print("After shuffle:")
    print(x)

if __name__ == "__main__":
    main()

2. Only First Axis¶

Rows are permuted, but elements within each row maintain their order.

np.random.permutation¶

Returns a permuted copy, leaving the original unchanged.

1. From Integer¶

import numpy as np

def main():
    x = np.random.permutation(10)
    print(x)  # Random permutation of 0-9

if __name__ == "__main__":
    main()

2. From List¶

import numpy as np

def main():
    x = [1, 4, 9, 12, 15]
    y = np.random.permutation(x)
    print(y)
    print(x)  # Original unchanged

if __name__ == "__main__":
    main()

3. From Array¶

import numpy as np

def main():
    x = np.array([1, 4, 9, 12, 15])
    y = np.random.permutation(x)
    print(y)

if __name__ == "__main__":
    main()

2D Permutation¶

Like shuffle, permutation operates along the first axis for 2D arrays.

1. Row Permutation¶

import numpy as np

def main():
    x = np.arange(9).reshape((3, 3))
    y = np.random.permutation(x)
    print(y)

if __name__ == "__main__":
    main()

2. Non-Destructive¶

The original array x remains unchanged.

shuffle vs permutation¶

Choose based on whether you need the original array.

1. Use shuffle¶

When you want to modify the array in-place to save memory.

2. Use permutation¶

When you need to preserve the original array or create an index array.

3. Memory Trade-off¶

shuffle is memory-efficient; permutation creates a copy.

Common Applications¶

Random permutations are essential in many algorithms.

1. Data Shuffling¶

Randomize training data order before each epoch in machine learning.

2. Random Sampling¶

Create random indices for selecting subsets of data.

3. A/B Testing¶

Randomly assign subjects to control and treatment groups.