Web20 jan. 2016 · Convert your base list to a numpy array and then apply another list as an index: >>> from numpy import array >>> array (aList) [myIndices] array ( ['a', 'd', 'e'], dtype=' S1') If you need, convert back to a list at the end: >>> from numpy import array >>> a = array (aList) [myIndices] >>> list (a) ['a', 'd', 'e'] Web20 mrt. 2016 · Using np.array instead of np.matrix allows you to do a simple mask indexing like: a = a [a [:, 2] != 0] to convert from np.matrix to np.array you can do: a = np.asarray (a) Share Improve this answer Follow answered Mar 20, 2016 at 15:52 Saullo G. P. Castro 56.1k 26 176 234 4 a = a.A is an equivalent of a = np.asarray (a) for matrices. :) – MSeifert
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Web30 mei 2024 · To do this using numpy, ie, if you have an array, a, instead of list_a: a = np.array ( [1, 2, 4, 6]) my_filter = np.array ( [True, False, True, False], dtype=bool) a [my_filter] > array ( [1, 4]) Share Improve this answer Follow edited Sep 9, 2013 at 19:36 answered Sep 6, 2013 at 21:05 Alex Szatmary 3,381 3 20 28 3 Web15 mei 2024 · 1 In this task, you will be filtering out complex elements from an array. Create a (4,) array with values 3, 4.5, 3 + 5j and 0 using "np.array ()". Save it to a variable array Create a boolean condition real to retain only a real number using .isreal (array).
Web12 aug. 2014 · The above works because a != np.array (None) is a boolean array which maps out non-None values: In [20]: a != np.array (None) Out [20]: array ( [ True, True, True, True, True, True, True, True, True, False], dtype=bool) Selecting elements of an array in this manner is called boolean array indexing. Share Improve this answer Follow Webndarrays can be indexed using the standard Python x [obj] syntax, where x is the array and obj the selection. There are different kinds of indexing available depending on obj : …
Web3 apr. 2024 · The canonical way to filter is to construct a boolean mask and apply it on the array. That said, if it happens that the function is so complex that vectorization is not possible, it's better/faster to convert the array into a Python list (especially if it uses Python functions such as sum ()) and apply the function on it. Web12 jul. 2024 · Given a 2D matrix and an index accessing the matrix, it checks for out-of-bounds indices and returns the value at the given index. Otherwise, ... Assuming that your indices is a numpy array of size n X 2 where n is the number of indices and 2 corresponds to two dimensions then you can use. index = np.random.randint(0,500, size= ...
Web15 jun. 2024 · You can use the following methods to filter the values in a NumPy array: Method 1: Filter Values Based on One Condition #filter for values less than 5 my_array …
Web5 nov. 2013 · numpy.take can be useful and works well for multimensional arrays. import numpy as np filter_indices = [1, 2] array = np.array([[1, 2, 3, 4, 5], [10, 20, 30, 40, 50], … 颯 の つく 苗字Web10 okt. 2013 · In numpy you could do this with a boolean indexing: a = np.arange (9, -1, -1) # a = array ( [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]) b = a [np.arange (len (a))!=3] # b = array ( [9, 8, 7, 5, 4, 3, 2, 1, 0]) which will, in general, be much faster than the list comprehension listed above. Share Improve this answer edited Oct 10, 2013 at 21:29 tarif 7311Web13 okt. 2024 · The numpy.any() function tests whether any array elements along the mentioned axis evaluate to True. Syntax : numpy.any(a, axis = None, out = None, … 颯 の意味