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. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having numpy version 1.18.Three Answers Compared For Coding Ease And Speed With this, we come to the end of this tutorial. Here you can see that the 2-D numpy array gets sorted such that the columns are sorted.įor more on the numpy sort function, refer to its official documentation. To sort a 2-D numpy array column-wise, pass axis=0 to the function. You can control the sorting axis via the axis parameter which is -1 by default. That is, in the returned array, we see that the rows are sorted. You can see that by default, the numpy.sort() function sorts the array row-wise. You can also sort two-dimensional numpy arrays using the numpy sort function. Here, np.sort(arr) returns a sorted copy of the original array in ascending order which is then reversed using the slicing operator with a -1 step size. See the example below: import numpy as np But you can use slicing to reverse the order of a sorted array. By default, it sorts the array in ascending order. You can see that the numpy sort() function doesn’t come which an explicit argument for sorting the array in ascending or descending order. The different sorting algorithms give the same result, what changes is the under the hood operations to determine the order of elements inside the array. # sort the array with different algorithmsĪrr_sorted1 = np.sort(arr, kind='quicksort')Īrr_sorted2 = np.sort(arr, kind='mergesort')Īrr_sorted3 = np.sort(arr, kind='heapsort')Īrr_sorted4 = np.sort(arr, kind='stable') Let’s look at the result of using different sorting algorithms in the numpy.sort() function. It determines which fields to compare first. order: Used in numpy arrays with defined fields.The available options are 'quicksort', 'mergesort', 'heapsort', and 'stable'. Defaults to -1, that is, sort along the last axis. axis: The axis along which to sort the array.The numpy ndarray sort() and the numpy sort() function take additional arguments – axis, kind, and order. Customizations in the numpy sort function You can see that the original array arr remains unchanged. On the other hand, if you do not want to alter the original array while sorting and would like the sorted array returned as a copy instead, use the global numpy.sort() function. In the above example, you can see that numpy array arr gets sorted in-place, that is, the original array gets modified when using the numpy ndarray sort() function. We can use the numpy ndarray sort() function to sort a one-dimensional numpy array. Let’s look at some examples and use-cases of sorting a numpy array. Here, arr is a numpy array (that is, a numpy ndarray object). The following is the syntax: import numpy as np You can also use the global numpy.sort() function which returns a copy of the sorted array. You can use the numpy ndarray function sort() to sort a numpy array. In this tutorial, we’ll look at how to sort a numpy array in python along with some of its common use-cases.
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