of class methods before moving on to modulesthoughbe sure to work through this quiz and the exercises for this part of the bookto practice what we've learned about functions here test your knowledgequiz what conclusions can you draw from this about the relative speed of python iteration tools what conclusions can you draw from this about the relative speed of the pythons timedtest your knowledgeanswers in generallist comprehensions are usually the quickest of the bunchmap beats list comprehensions in python only when all tools must call functionsfor loops tend to be slower than comprehensionsand generator functions and expressions are slower than comprehensions by constant factor under pypysome of these findings differmap often turns in different relative performancefor exampleand list comprehensions seem always quickestperhaps due to function-level optimizations at least that' the case today on the python versions testedon the test machine usedand for the type of code timed--these results may vary if any of these three variables differ use the homegrown timer or standard library timeit to test your use cases for more relevant results also keep in mind that iteration is just one component of program' timemore code gives more complete picture in generalpypy (implementing python is typically faster than cpython and cpython is often faster than cpython in most cases timedpypy is some faster than cpythonand cpython is often small constant faster than cpython in cases that use integer mathcpython can be faster than cpython and pypy can be faster than in other cases ( string operations and file iterators)pypy can be slower than cpython by xthough timeit and memory management differences may influence some results the pystone benchmark confirms these relative rankingsthough the sizes of the differences it reports differ due to the code timed at least that' the case today on the python versions testedon the test machine usedand for the type of code timed--these results may vary if any of these three variables differ use the homegrown timer or standard library timeit to test your use cases for more relevant results this is especially true when timing python implementationswhich may be arbitrarily optimized in each new release the benchmarking interlude