When writing efficient Python code, it's often useful to compare the execution times of different implementations of the same functionality. This can help us identify which implementation is faster and whether the performance improvements are significant enough to justify the added complexity.
One way to compare execution times is to use the time module. Here's an example:
import time# Implementation 1start = time.time()# do somethingend = time.time()print("Implementation 1 took", end - start, "seconds")# Implementation 2start = time.time()# do something elseend = time.time()print("Implementation 2 took", end - start, "seconds") |
This code uses the time.time() function to record the start and end times of each implementation, and then prints the difference between them. By running both implementations and comparing the execution times, we can get a sense of which implementation is faster.
However, this method has some limitations. First, the execution times can be affected by other processes running on the system, so the results may not be consistent across different runs. Second, the time module only provides a coarse-grained measurement of time, so it may not be precise enough to detect small differences in execution times.
To get more accurate and consistent results, we can use the %timeit magic command in Jupyter notebooks, as described in the previous examples. This command runs the code multiple times and provides more detailed timing information, which can help us make more informed decisions about which implementation to use.