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In Python, choosing specific elements from a list based on their index can be a common requirement. A recently posed question addressed this scenario, presenting a list of integers a and a list of indices b. The goal was to create a new list c containing elements of a corresponding to the indices in b.
The provided solution utilized a list comprehension that iterates over the indices in b and selects the appropriate elements from a:
a = [-2, 1, 5, 3, 8, 5, 6] b = [1, 2, 5] c = [a[i] for i in b]
While this approach is perfectly valid, the question arose whether there exist more efficient methods for achieving the desired result.
Operator.itemgetter
One alternatif solution involves the use of the operator.itemgetter function from Python's operator module:
from operator import itemgetter a = [-2, 1, 5, 3, 8, 5, 6] b = [1, 2, 5] print(itemgetter(*b)(a))
Numpy Array Indexing
For cases where performance is critical, NumPy offers optimized array handling capabilities:
import numpy as np a = np.array([-2, 1, 5, 3, 8, 5, 6]) b = [1, 2, 5] print(list(a[b]))
Evaluation of Solutions
Notably, the original solution using list comprehension remains a viable option. It is concise and easy to understand. The itemgetter and NumPy methods, while offering potential performance benefits, may be overkill for smaller lists or situations where speed is less of a concern.
Ultimately, the choice of which method to use depends on factors such as the size of the list and the specific performance requirements of the application.
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