Solution 2: Using np.argsort() with Slicing for Sorting and Indexing

Problem:

• You have a NumPy array containing various numbers.
• You want to identify the indices (positions) of the N largest values within this array.
• This is useful for tasks like:
  • Finding the top-performing students in a class based on their grades
  • Identifying the most frequently used words in a text document
  • Determining the most active pixels in an image

Key Concepts:

• NumPy: A powerful Python library for numerical computations and array manipulations.
• Indices: Numerical positions of elements within an array, starting from 0.
• Maximum Values: The largest numerical values within the array.

Solutions:

1. Using np.argpartition():

   • Efficiently partitions the array based on the values, placing the N largest values towards the end.
   • Example:

   import numpy as np
   
   arr = np.array([3, 1, 4, 2, 5, 7, 8, 6])
   N = 3  # Find indices of top 3 maximum values
   
   top_N_indices = np.argpartition(arr, -N)[-N:]
   print(top_N_indices)  # Output: [6 5 7]
   

2. Using np.argsort() with Slicing:

   • Sorts the indices based on the array values in ascending order.
   • Slices the sorted indices to get the indices of the N largest values.

   sorted_indices = np.argsort(arr)[::-1]  # Sort in descending order
   top_N_indices = sorted_indices[:N]
   print(top_N_indices)  # Output: [6 5 7]
   

Additional Notes:

• For repeated maximum values, both methods return all their indices.
• For large arrays, np.argpartition() is generally faster than np.argsort().
• To retrieve the actual maximum values, use arr[top_N_indices].