Both work differently. The list comprehension version takes advantage of the special bytecode LIST_APPEND which calls PyList_Append directly for us. Hence it avoids an attribute lookup to list.append and a function call at the Python level. >>> def func_lc(): [x**2 for x in y] … >>> dis.dis(func_lc) 2 0 LOAD_CONST 1 (<code object <listcomp> at … Read more
Yes, your understanding is correct. There is basically (very basically) a giant switch statement inside the CPython interpreter that says “if the current opcode is so and so, do this and that”. http://hg.python.org/cpython/file/3.3/Python/ceval.c#l790 Other implementations, like Pypy, have JIT compilation, i.e. they translate Python to machine codes on the fly.
This has to do with how is works. It checks for references instead of value. It returns True if either argument is assigned to the same object. In this case, they are different instances; float(0) and float(0) have the same value ==, but are distinct entities as far as Python is concerned. CPython implementation also … Read more
Note: exec was just a Simple statement in Python 2.x, whereas it is a function in Python 3.x. Python 2.7 Let us check the changes made by executing a. class Test: def __init__(self): l, g = locals().copy(), globals().copy() exec a # NOT a function call but a statement print locals() == l, globals() == g … Read more
In order to optimize set(list_2), the interpreter needs to prove that list_2 (and all of its elements) does not change between iterations. This is a hard problem in the general case, and it would not surprise me if the interpreter does not even attempt to tackle it. On the other hand a set literal cannot … Read more
This helps the interpreter assure that the keys for class-level attributes and methods can only be strings. Elsewhere, Python is a “consenting adults language”, meaning that dicts for objects are exposed and mutable by the user. However, in the case of class-level attributes and methods for classes, if we can guarantee that the keys are … Read more
One important thing about this behavior is that Python caches some, mostly, short strings (usually less than 20 characters but not for every combination of them) so that they become quickly accessible. One important reason for that is that strings are widely used in Python’s source code and it’s an internal optimization to cache some … Read more
[*] TL;DR: This “empty __class__ cell” error will happen when the metaclass tries to call a method in the defined class (or instantiate it) before it is done with its __new__ and __init__,and the called method uses super. The error will also happen if one writes a call to super() in a function defined outside … Read more
See the Rationale section from PEP 424 that introduced __length_hint__ and offers insight on the motivation: Being able to pre-allocate lists based on the expected size, as estimated by __length_hint__ , can be a significant optimization. CPython has been observed to run some code faster than PyPy, purely because of this optimization being present. In … Read more
In an ideal world, you’d be right and some of the inconsistencies you found would be wrong. However, CPython has optimized some scenarios, specifically function locals. These optimizations, together with how the compiler and evaluation loop interact and historical precedent, lead to the confusion. Python translates code to bytecodes, and those are then interpreted by … Read more