In general, list comprehensions are usually the quickest of the bunch; map beats list comprehensions in Python only when all tools must call functions; for loops tend to be slower than comprehensions; and generator functions and expressions are slower than comprehensions by a constant factor. Under PyPy, some of these findings differ; map often turns in a different relative performance, for example, and list comprehensions seem always quickest, perhaps due to function-level optimizations.
At least that’s the case today on the Python versions tested, on the test machine used, and 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 a program’s time: more code gives a more complete picture.
In general, PyPy 1.9 (implementing Python 2.7) is typically faster than CPython 2.7, and CPython 2.7 is often faster than CPython 3.3. In most cases timed, PyPy is some 10X faster than CPython, and CPython 2.7 is often a small constant faster than CPython 3.3. In cases that use integer math, CPython 2.7 can be 10X faster than CPython 3.3, and PyPy can be 100X faster than 3.3. In other cases (e.g., string operations and file iterators), PyPy can be slower than CPython by 10X, though timeit and memory management differences may influence some results. The pystone benchmark confirms these relative rankings, though the sizes of the differences it reports differ due to the code timed.
At least that’s the case today on the Python versions tested, on the test machine used, and 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 implementations, which may be arbitrarily optimized in each new release.