Beyond logical_or: Efficient Techniques for Multi-Array OR Operations in NumPy

Here are two common approaches:

Here's an example using reduce to achieve logical OR on three arrays:

import numpy as np

# Create three sample arrays
arr1 = np.array([True, False, True, False])
arr2 = np.array([False, True, False, True])
arr3 = np.array([True, True, False, False])

# Combine using reduce with logical_or
result = np.logical_or.reduce([arr1, arr2, arr3])

# Print the result
print(result)

This code will output:

[ True  True  True  True]

Both arr1[i] OR arr2[i] OR arr3[i] will be True if any of the elements in that position are True, resulting in True in the corresponding position of the output array.

Recursive approach:

import numpy as np

def logical_or_recursive(arr1, arr2, *args):
  """
  Performs logical OR operation recursively on multiple NumPy arrays.

  Args:
      arr1, arr2 (np.ndarray): First two arrays for comparison.
      *args (np.ndarray): Additional arrays for comparison.

  Returns:
      np.ndarray: Resulting array after applying logical OR operation.
  """
  if not args:
    return np.logical_or(arr1, arr2)
  else:
    return logical_or_recursive(np.logical_or(arr1, arr2), args[0], *args[1:])

# Sample arrays
arr1 = np.array([True, False, True, False])
arr2 = np.array([False, True, False, True])
arr3 = np.array([True, True, False, False])

# Combine using recursion
result = logical_or_recursive(arr1, arr2, arr3)

# Print the result
print(result)

Reduce function approach:

import numpy as np

# Sample arrays
arr1 = np.array([True, False, True, False])
arr2 = np.array([False, True, False, True])
arr3 = np.array([True, True, False, False])

# Combine using reduce with logical_or
result = np.logical_or.reduce([arr1, arr2, arr3])

# Print the result
print(result)

Both codes achieve the same result of performing a logical OR operation on all three arrays. The recursive approach offers more flexibility but might be slower for very large datasets. The reduce function approach is generally the more efficient and concise option.

List comprehension:

import numpy as np

# Sample arrays
arr1 = np.array([True, False, True, False])
arr2 = np.array([False, True, False, True])
arr3 = np.array([True, True, False, False])

# Combine using list comprehension
result = [a or b or c for a, b, c in zip(arr1, arr2, arr3)]

# Convert list to NumPy array (optional)
result_array = np.array(result)

# Print the result
print(result_array)

This code iterates through corresponding elements in each array using zip and applies the logical OR operation using list comprehension. Finally, it converts the resulting list to a NumPy array (optional).

Vectorized operations with broadcasting:

import numpy as np

# Sample arrays (ensure compatible shapes for broadcasting)
arr1 = np.array([True, False, True, False])[:, None]
arr2 = np.array([False, True, False, True])[:, None]
arr3 = np.array([True, True, False, False])[:, None]

# Combine using broadcasting
result = np.logical_or(arr1, arr2, arr3)

# Print the result
print(result)

This approach utilizes NumPy's broadcasting to perform the logical OR operation element-wise across all arrays. Reshaping the arrays with [:, None] ensures they have compatible shapes for broadcasting.

Both list comprehension and vectorized operations offer efficient ways to achieve the logical OR on multiple NumPy arrays. The choice depends on your preference and the specific context of your code.