Efficient way to take the minimum/maximum n values and indices from a matrix using NumPy

Question

What\'s an efficient way, given a NumPy matrix (2D array), to return the minimum/maximum n values (along with their indices) in the array?

Currently I h

Answer 1

Since the time of the other answer, NumPy has added the numpy.partition and numpy.argpartition functions for partial sorting, allowing you to do this in O(arr.size) time, or O(arr.size+n*log(n)) if you need the elements in sorted order.

numpy.partition(arr, n) returns an array the size of arr where the nth element is what it would be if the array were sorted. All smaller elements come before that element and all greater elements come afterward.

numpy.argpartition is to numpy.partition as numpy.argsort is to numpy.sort.

Here's how you would use these functions to find the indices of the minimum n elements of a two-dimensional arr:

flat_indices = numpy.argpartition(arr.ravel(), n-1)[:n]
row_indices, col_indices = numpy.unravel_index(flat_indices, arr.shape)

And if you need the indices in order, so row_indices[0] is the row of the minimum element instead of just one of the n minimum elements:

min_elements = arr[row_indices, col_indices]
min_elements_order = numpy.argsort(min_elements)
row_indices, col_indices = row_indices[min_elements_order], col_indices[min_elements_order]

---

The 1D case is a lot simpler:

# Unordered:
indices = numpy.argpartition(arr, n-1)[:n]

# Extra code if you need the indices in order:
min_elements = arr[indices]
min_elements_order = numpy.argsort(min_elements)
ordered_indices = indices[min_elements_order]

Answer 2

I just met the exact same problem and solved it.
Here is my solution, wrapping the np.argpartition:

• Applied to arbitrary axis.
• High speed when K << array.shape[axis], o(N).
• Return both the sorted result and the corresponding indexs in original matrix.

def get_sorted_smallest_K(array, K, axis=-1):
    # Find the least K values of array along the given axis. 
    # Only efficient when K << array.shape[axis].
    # Return:
    #   top_sorted_scores: np.array. The least K values.
    #   top_sorted_indexs: np.array. The least K indexs of original input array.
    
    partition_index = np.take(np.argpartition(array, K, axis), range(0, K), axis)
    top_scores = np.take_along_axis(array, partition_index, axis)
    sorted_index = np.argsort(top_scores, axis=axis)
    top_sorted_scores = np.take_along_axis(top_scores, sorted_index, axis)
    top_sorted_indexs = np.take_along_axis(partition_index, sorted_index, axis)
    return top_sorted_scores, top_sorted_indexs

Answer 3

Since there is no heap implementation in NumPy, probably your best guess is to sort the whole array and take the last n elements:

def n_max(arr, n):
    indices = arr.ravel().argsort()[-n:]
    indices = (numpy.unravel_index(i, arr.shape) for i in indices)
    return [(arr[i], i) for i in indices]

(This will probably return the list in reverse order compared to your implementation - I did not check.)

A more efficient solution that works with newer versions of NumPy is given in this answer.