numpy-ndarray

I have a ndarray containing the dictionary keys arranged in a specific order. I want to create another ndarray containing the values of the respective keys. The order have to be maintained. Obvious approach is to iterate over the array containing the keys element by element but the problem is there is no way to know the shape of the array beforehand. Is it possible to flatten the ndarray of keys and iterate over it to generate flat ndarray of values and finally unravel it without harming the order? mydict = {'a':1, 'b':2, 'c':3, 'd':4, 'e':5, 'f':6} input_pattern = np.array([['a', 'f'], ['b',

I have the following numpy array import numpy as np a = np.array([1,2,6,8]) I want to create another numpy array from a such that it contains all the different possible sums of TWO elements of a . It's easy to show then that there are int(a.size*(a.size-1)/2) different possible sums, composed from: a[0] + a[1] a[0] + a[2] a[0] + a[3] a[1] + a[2] a[1] + a[3] a[2] + a[3] How can I construct a numpy array with the above sums as elements without using a double for loop (the only way I can think of it). For the above example, the output should be [3,7,9,8,10,14] MWE eff = int(a.size*(a.size-1)/2) c

As in title, I need to perform numpy.exp on a very large ndarray, let's say ar , and store the result in ar itself. Can this operation be performed in-place? You can use the optional out argument of exp : a = np.array([3.4, 5]) res = np.exp(a, a) print(res is a) print(a) Output: True [ 29.96410005 148.4131591 ] exp(x[, out]) Calculate the exponential of all elements in the input array. Returns out : ndarray Output array, element-wise exponential of x . Here all elements of a will be replaced by the result of exp . The return value res is the same as a . No new array is created kmario23 Mike

Using this small reproducible example, I've so far been unable to generate a new integer array from 3 arrays that contains unique groupings across all three input arrays. The arrays are related to topographic properties: import numpy as np asp = np.array([8,1,1,2,7,8,2,3,7,6,4,3,6,5,5,4]).reshape((4,4)) #aspect slp = np.array([9,10,10,9,9,12,12,9,10,11,11,9,9,9,9,9]).reshape((4,4)) #slope elv = np.array([13,14,14,13,14,15,16,14,14,15,16,14,13,14,14,13]).reshape((4,4)) #elevation The idea is that the geographic contours are broken into 3 different properties using GIS routines: 1-8 for aspect

I know similar questions have been asked before ( e.g. ), but AFAIK nobody has answered my specific question... My question is about the numpy mixed advanced / basic indexing described here : ... Two cases of index combination need to be distinguished: The advanced indexes are separated by a slice, ellipsis or newaxis. For example x[arr1,:,arr2] . The advanced indexes are all next to each other. For example x[...,arr1,arr2,:] but not x[arr1,:,1] since 1 is an advanced index in this regard. In the first case, the dimensions resulting from the advanced indexing operation come first in the result