int has lots of bases. *, 0*, 0x*, 0b*, 0o* and it can be long, it takes time to determine the base and other things if the base is set, it saves a lot of time python -m timeit “int(‘1’,10)” 1000000 loops, best of 3: 0.252 usec per loop python -m timeit “int(‘1’)” 1000000 loops, … Read more
There are two meanings of “built-in” here, although they both mean “part of the interpreter”. The library reference uses it to indicate that a function is available without an import (it is “not part of a module”, although see builtins). The interpreter itself uses it to indicate that a function is implemented natively rather than … Read more
In a tl;dr fashion: This is because two functions exist for the two different cases, as can be seen in unicode_strip; do_strip and _PyUnicodeXStrip the first executing much faster than the second. Function do_strip is for the common case str.strip() where no arguments exist and do_argstrip (which wraps _PyUnicode_XStrip) for the case where str.strip(arg) is … Read more
Because in the course of normal operations Python will create and destroy a lot of small tuples, Python keeps an internal cache of small tuples for that purpose. This helps cut down on a lot of memory allocation and deallocation churn. For the same reasons small integers from -5 to 255 are interned (made into … Read more
Remember that the Python int type does not have a limited range like C int has; the only limit is the available memory. Memory goes to storing the value, the current size of the integer storage (the storage size is variable to support arbitrary sizes), and the standard Python object bookkeeping (a reference to the … Read more
Because using str(obj) must first go through type.__call__ then str.__new__ (create a new string) then PyObject_Str (make a string out of the object) which invokes int.__str__ and, finally, uses the function you linked. repr(obj), which corresponds to builtin_repr, directly calls PyObject_Repr (get the object repr) which then calls int.__repr__ which uses the same function as … 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
Why 28 bytes initially for any value as low as 1? I believe @bgusach answered that completely; Python uses C structs to represent objects in the Python world, any objects including ints: struct _longobject { PyObject_VAR_HEAD digit ob_digit[1]; }; PyObject_VAR_HEAD is a macro that when expanded adds another field in the struct (field PyVarObject which … Read more
Immutable objects don’t have the same id, and as a matter of fact this is not true for any type of objects that you define separately. Generally speaking, every time you define an object in Python, you’ll create a new object with a new identity. However, for the sake of optimization (mostly) there are some … Read more
The problem being solved is that the previous hash algorithm in Lib/sets.py had horrendous performance on datasets that arise in a number of graph algorithms (where nodes are represented as frozensets): # Old-algorithm with bad performance def _compute_hash(self): result = 0 for elt in self: result ^= hash(elt) return result def __hash__(self): if self._hashcode is … Read more