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TL; DR: use np.where as it is the fastest option. Your options are np.where, itertools.compress, and list comprehension.. See the detailed comparison below, where it can be seen np.where outperforms both itertools.compress and also list comprehension. >>> from itertools import compress >>> import numpy as np >>> t = [False, False, False, False, True, True, False, True, False, False, False ...
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BetaParameters: condition array_like, bool. Where True, yield x, otherwise yield y.. x, y array_like. Values from which to choose. x, y and condition need to be broadcastable to some shape.. Returns: out ndarray. An array with elements from x where condition is True, and elements from y elsewhere.
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BetaYou can use the following methods to get the indices where some condition is true in NumPy: Method 1: Get Indices Where Condition is True in NumPy Array. #get indices of values greater than 10 np. asarray (my_array> 10). nonzero () Method 2: Get Indices Where Condition is True in NumPy Matrix
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BetaLearn how to use boolean indexing to get the indices for true conditions in NumPy arrays and matrices. See examples of one-dimensional and two-dimensional data, and how to apply conditions to specific rows or columns.
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BetaMethod #5: Using list.index() You can use the list.index() method to find the index of the first occurrence of a True value in the list. Then, you can use a loop to find the indices of all subsequent True values by calling list.index() again on the sublist starting from the next index. Here is an example of how to use this method: Python3
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BetaCreate a boolean matrix with numpy. Let's first create a random boolean matrix with False and True values. import numpy as np A = np.full((5, 5), False) n = 6 index = np.random.choice(A.size, n, replace=False) A.ravel()[index] = True print(A). returns for example [[False False False False False] [False True False False False] [False True True False False] [ True False False False False] [False ...
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BetaThe index [0] following np.where(sample_array == True) is used to select the first element of this tuple, which is the array of indices we’re interested in. Finally, the resulting indices are printed out. These indices point to the positions in the original sample_array where the value is True.
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BetaFAQs on Top 3 Methods to Locate Indices of True Values in NumPy Arrays Q: How do I use boolean indexing in NumPy? A: Boolean indexing is achieved by creating a boolean array where each condition is evaluated, and then using that array to index into the original array, returning the values that satisfy the condition.
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BetaTime complexity: O(n), where n is the number of elements in the boolean list. Space complexity: O(m), where m is the number of true values in the boolean list. Method #2: Using enumerate() method enumerate() method hashes the index with its value and coupled with list comprehension can let us check for the true values. Python3
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BetaEnumerate. for i in range(len(y)): is an antipattern. If you want indices (i) and the values at those indices (y[i]) the Pythonic way is using enumerate:for i, yi in enumerate(y): Start indices. Let's take a moment and split this task into two parts: the start indices and the stop indices.
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