Efficiently Selecting Specific Columns per Row in NumPy Using Lists or Boolean Arrays
NumPy offers extensive capabilities for manipulating multidimensional arrays. However, selecting specific columns based on a list of indexes for each row can be challenging and demand efficient solutions.
One approach to solving this issue is to utilize boolean arrays. Each column of a boolean array can represent the desired selection for a particular row. By using direct selection with the boolean array, specific columns can be extracted efficiently. For instance:
<code class="python">import numpy as np a = np.array([1, 2, 3]) b = np.array([[False, True, False], [True, False, False], [False, False, True]]) a[b] # Output: [2, 4, 9]</code>
Alternatively, it's possible to create an array representing the range of columns and use direct selection on it. This approach, however, may not always be optimal.
<code class="python">a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) a[np.arange(len(a)), [1, 0, 2]] # Output: [2, 4, 9]</code>
By leveraging these methods, it's feasible to efficiently select specific columns per row in NumPy arrays, regardless of whether the selection criteria are provided as a list of indexes or a boolean array.
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