rev 2021.1.18.38333, Stack Overflow works best with JavaScript enabled, Where developers & technologists share private knowledge with coworkers, Programming & related technical career opportunities, Recruit tech talent & build your employer brand, Reach developers & technologists worldwide, Your question is not really well defined. Similar with np, torch.topk also accepts an axis argument so that you can handle multi-dimensional arrays/tensors. For example. We'll use NumPy's random number generator, which we will seed with a set value in order to ensure that the same random arrays are generated each time this code is run: In [1]: import numpy as np np . Why is “1000000000000000 in range(1000000000000001)” so fast in Python 3? ; The return value of min() and max() functions is based on the axis specified. numpy.maximum() function is used to find the element-wise maximum of array elements. The list of indices that is returned has length equal exactly to k. If you have duplicates, they are grouped into a single tuple. random . If one of the elements being compared is a NaN, then that element is returned. In the case of multiple occurrences of the maximum values, the indices corresponding to the first occurrence are returned. off99555's answer was the most elegant, but it is the slowest. The indexes in NumPy arrays start with 0, meaning that the first element has index 0, and the second has index 1 etc. To get the indices of unique values in numpy array, pass the return_index argument in numpy.unique (), along with array i.e. @AndrewHundt : simply use (-arr).argsort(axis=-1)[:, :n], I think you can simplify the indexing here by using, FWIW, your solution won't provide unambiguous solution in all situations. OP should describe how to handle these unambiguous cases. Then 10 < 19, which means the index of 19 had returned, which is 1. There is argmin() and argmax() provided by numpy that returns the index of the min and max of a numpy array respectively. In the second case, we have passed arr and axis=0, which returns an array of size 3 contain. In this we are specifically going to talk about 2D arrays. python+numpy: efficient way to take the min/max n values and indices from a matrix, docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html, jakevdp.github.io/PythonDataScienceHandbook/…, Podcast 305: What does it mean to be a “senior” software engineer, index of N highest elements from a list of numpy array. The idea is, that the unique method returns the indices of the input values. random . Thanks, @eat The OP's question is a little ambiguous. This resultant array contains the indices of the maximum values element’s representative index number. Did "Antifa in Portland" issue an "anonymous tip" in Nov that John E. Sullivan be “locked out” of their circles because he is "agent provocateur"? (since k being positive or negative works the same for me! Let’s find the maximum value along a given axis. For example, what would the indices (you expect) to be for. # Get the minimum value from complete 2D numpy array minValue = numpy.amin(arr2D) It will return the minimum value from complete 2D numpy arrays i.e. # Create a numpy array from a list of numbers arr = np.array([11, 12, 13, 14, 15, 16, 17, 15, 11, 12, 14, 15, 16, 17]) # Get the index of elements with value less than 16 and greater than 12 result = np.where((arr > 12) & (arr < 16)) print("Elements with value less than 16 … for the i value, take all values (: is a full slice, from start to end) for the j value take 1; Giving this array [2, 5, 8]: The array you get back when you index or slice a numpy array is a view of the original array. In the above code, we are checking the maximum element along with the x-axis. Learn how your comment data is processed. This site uses Akismet to reduce spam. It compares two arrays and returns a new array containing the element-wise maxima. This resultant array is hat of the same dimensions and shape of that of the array a1, but with the dimensions along the specified axis being removed as an exception. To get the indices of the four largest elements, do To get the indices of the four largest elements, do :) The OP should simply refer to the definition of np.argmax, Well, one might consider the implementation of. It is the same data, just accessed in a different order. Then from the max unique value and the indicies, the position of the original values can be recreated. Example 1: Get Maximum Value of Numpy Array In this example, we will take a numpy array with random numbers and then find the maximum of the array using numpy.max() function. I found it most intuitive to use np.unique. Example. @FredFoo: why did you use -4? Which you could fix (if needed) by making a copy or replacing back the original values. The value to use for missing values. Replacements for switch statement in Python? And you can log the original value of these elements and recover them if you want. it only prints the smallest numbers first! in all rows and columns. from numpy import unravel_index result = unravel_index(np.max(array_2d),array_2d.shape) print("Index for the Maximum Value in the 2D Array is:",result) Index for the Maximum Value in 2D Array We can see that the maximum element of this array is 14, which is at position 1, so the output is 1. If not, do you perhaps know how? To ignore NaN values (MATLAB behavior), please use nanmax. Apart from doing a sort manually after np.argpartition, my solution is to use PyTorch, torch.topk, a tool for neural network construction, providing NumPy-like APIs with both CPU and GPU support. In NumPy, we have this flexibility, we can remove values from one array and add them to another array. Save my name, email, and website in this browser for the next time I comment. numpy.maximum¶ numpy.maximum (x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True [, signature, extobj]) =

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