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Cool, this is another valid way of doing it and I suppose it comes down to preference. A few notes I'd like to mention though, that are not relevant to our toy examples, but may have implications in a production environment: 1. Unless you intend to store the resulting list from a list comprehension, it's good practice to prefer generator comprehensions because they don't grow in memory as the number of iterations increases. Obviously in our examples the sample size is so small that it doesn't matter, but the difference between ''.join(x for x in y) and ''.join([x for x in y]) can grow very large if y consists of many thousands of items. Here's an example with just integers which requires over a million items before it starts becoming a concern – ultimately it depends on the memory footprint of each item: In [2]: %memit sum(x for x in xrange(1234567))
peak memory: 46.87 MiB, increment: 0.00 MiB
In [3]: %memit sum([x for x in xrange(1234567)])
peak memory: 68.52 MiB, increment: 11.78 MiB
2. I still prefer the list of tuples in this situation as you have control of word order (you may not want to go through the factors in ascending order). There's [x, z].insert(1, y) which would change the list to [x, y, z] to avoid having to add to the end. Finally, because it's a list you can very easily do a one-time cost in-place sort: [(5, 'Buzz'), (3, 'Fizz')].sort(key=lambda t: t[0])
Again, it's silly to talk about performance in our toy examples, but small details like these can actually have memory and execution time implications if you don't consider the differences.Ultimately, the best advice is to always write the code as readable and maintainable as possible and then optimize. Especially when dealing with a language like Python which was built for expressiveness, not leading performance. |