Return top_sorted_scores, top_sorted_indexesįrom trics. Top_sorted_indexes = np.take_along_axis(partition_index, sorted_index, axis) Top_sorted_scores = np.take_along_axis(top_scores, sorted_index, axis) Sorted_index = np.flip(sorted_index, axis=axis) Sorted_index = np.argsort(top_scores, axis=axis) Top_scores = np.take_along_axis(array, partition_index, axis) Partition_index = np.take(np.argpartition(array, kth=top_k, axis=axis), range(0, top_k), axis) Range(axis_length - top_k, axis_length), axis) Partition_index = np.take(np.argpartition(array, kth=-top_k, axis=axis), import numpy as npĭef get_sorted_top_k(array, top_k=1, axis=-1, reverse=False): When top_k<<axis_length,it better than argsort. Returns the indices of the k first largest elements of arrĪssert k <= arr.size, 'k should be smaller or equal to the array size'Īrr_ = arr.astype(float) # make a copy of arr In : np.argpartition(a, np.argmin(a, axis=0)) In : np.argpartition(a, np.argmin(a, axis=0)) # 1 is because the first item is the minimum one. Here is an example: In : a = np.random.randint(0, 20, (10, 10))Īrray(, In that case you can use np.argsort() along the intended axis: indices = np.argsort(arr, axis=1) Indices = np.argpartition(arr, -N, axis=1)Īnd for grabbing the items: x = arr.shapeĪrr.reshape(x, N)īut note that this won't return a sorted result. Off99555's Answer: įor multidimensional arrays you can use the axis keyword in order to apply the partitioning along the expected axis. Result = nlargest(5, enumerate(a1), itemgetter(1)) ''' off99555's Answer - No Modification Needed For My Needs ''' ''' Fred Foos Answer Modified A Bit For My Case''' ''' NPE's Answer Modified A Bit For My Case ''' off99555's answer was the most elegant, but it is the slowest.Ĭomplete Code for Test and Comparisons import numpy as np. I went with this answer, because even though it took more work, it was not too bad and had significant speed advantages. Fred Foos answer required the most refactoring for my needs but was the fastest.NPE's answer was the next most elegant and adequately fast for my needs.I then compared the speed of each method. Speed was important for my needs, so I tested three answers to this question.Ĭode from those three answers was modified as needed for my specific case. Notice that the rows are now sorted in descending order (largest to smallest) based on the values in the second column.Three Answers Compared For Coding Ease And Speed We can use the following code to sort the rows of the NumPy array in descending order based on the values in the second column: #define new matrix with rows sorted in descending order by values in second column Example 2: Sort Numpy Array by Column Values Descending Notice that the rows are now sorted in ascending order (smallest to largest) based on the values in the second column. We can use the following code to sort the rows of the NumPy array in ascending order based on the values in the second column: #define new matrix with rows sorted in ascending order by values in second column Suppose we have the following NumPy array: import numpy as np Example 1: Sort Numpy Array by Column Values Ascending The following examples show how to use each method in practice. Method 2: Sort by Column Values Descending x_sorted_desc = x. a aa:,1.argsort(kind'mergesort') a aa:,0. Method 1: Sort by Column Values Ascending x_sorted_asc = x. You can sort on multiple columns as per Steve Tjoa's method by using a stable sort like mergesort and sorting the indices from the least significant to the most significant columns: a aa:,2.argsort() First sort doesn't need to be stable. You can use the following methods to sort the rows of a NumPy array by column values:
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