Optimizing Python code can significantly improve performance. Techniques include using efficient data structures, minimizing I/O operations, and leveraging libraries like NumPy.
Example optimization:
import time
start_time = time.time()
result = [i**2 for i in range(10000)]
print('Time taken:', time.time() - start_time)
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