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python, basic lambda function

I am a c++ guy, learning the lambda function in python and wanna know it inside out. did some seraches before posting here. anyway, this piece of code came up to me.
<1> i dont quite understand the purpose of lambda function here. r we trying to get a function template? If so, why dont we just set up 2 parameters in the function input?
<2> also, make_incrementor(42), at this moment is equivalent to return x+42, and x is the 0,1 in f(0) and f(1)?
<3> for f(0), does it not have the same effect as >>>f = make_incrementor(42)? for f(0), what are the values for x and n respectively?
any commments are welcome! thanks.
>>> def make_incrementor(n):
... return lambda x: x + n
...
>>> f = make_incrementor(42)
>>> f(0)
42
>>> f(1)
43

Yes, this is similar to a C++ int template. However, instead of at compile time (yes, Python (at least for CPython) is "compiled"), the function is created at run time. Why the lambda is used in this specific case is unclear, probably only for demonstration that functions can be returned from other functions rather than practical use. Sometimes, however, statements like this may be necessary if you need a function taking a specified number of arguments (e.g. for map, the function must take the same number of arguments as the number of iterables given to map) but the behaviour of the function should depend on other arguments.
make_incrementor returns a function that adds n (here, 42) to any x passed to that function. In your case the x values you tried are 0 and `1``
f = make_incrementor(42) sets f to a function that returns x + 42. f(0), however, returns 0 + 42, which is 42 - the returned types and values are both different, so the different expressions don't have the same effect.

The purpose is to show a toy lambda return. It lets you create a function with data baked in. I have used this less trivial example of a similar use.
def startsWithFunc(testString):
return lambda x: x.find(testString) == 0
Then when I am parsing, I create some functions:
startsDesctription = startsWithFunc("!Sample_description")
startMatrix = startsWithFunc("!series_matrix_table_begin")
Then in code I use:
while line:
#.... other stuff
if startsDesctription(line):
#do description work
if startMatrix(line):
#do matrix start work
#other stuff ... increment line ... etc
Still perhaps trival, but it shows creating general funcitons with data baked it.

How references to variables are resolved in Python [duplicate]

This question already has answers here:
Short description of the scoping rules?
(9 answers)
Closed 6 months ago.
This message is a a bit long with many examples, but I hope it
will help me and others to better grasp the full story of variables
and attribute lookup in Python 2.7.
I am using the terms of PEP 227
(http://www.python.org/dev/peps/pep-0227/) for code blocks (such as
modules, class definition, function definitions, etc.) and
variable bindings (such as assignments, argument declarations, class
and function declaration, for loops, etc.)
I am using the terms variables for names that can be called without a
dot, and attributes for names that need to be qualified with an object
name (such as obj.x for the attribute x of object obj).
There are three scopes in Python for all code blocks, but the functions:
Local
Global
Builtin
There are four blocks in Python for the functions only (according to
PEP 227):
Local
Enclosing functions
Global
Builtin
The rule for a variable to bind it to and find it in a block is
quite simple:
any binding of a variable to an object in a block makes this variable
local to this block, unless the variable is declared global (in that
case the variable belongs to the global scope)
a reference to a variable is looked up using the rule LGB (local,
global, builtin) for all blocks, but the functions
a reference to a variable is looked up using the rule LEGB (local,
enclosing, global, builtin) for the functions only.
Let me know take examples validating this rule, and showing many
special cases. For each example, I will give my understanding. Please
correct me if I am wrong. For the last example, I don't understand the
outcome.
example 1:
x = "x in module"
class A():
print "A: " + x #x in module
x = "x in class A"
print locals()
class B():
print "B: " + x #x in module
x = "x in class B"
print locals()
def f(self):
print "f: " + x #x in module
self.x = "self.x in f"
print x, self.x
print locals()
>>>A.B().f()
A: x in module
{'x': 'x in class A', '__module__': '__main__'}
B: x in module
{'x': 'x in class B', '__module__': '__main__'}
f: x in module
x in module self.x in f
{'self': <__main__.B instance at 0x00000000026FC9C8>}
There is no nested scope for the classes (rule LGB) and a function in
a class cannot access the attributes of the class without using a
qualified name (self.x in this example). This is well described in
PEP227.
example 2:
z = "z in module"
def f():
z = "z in f()"
class C():
z = "z in C"
def g(self):
print z
print C.z
C().g()
f()
>>>
z in f()
z in C
Here variables in functions are looked up using the LEGB rule, but if
a class is in the path, the class arguments are skipped. Here again,
this is what PEP 227 is explaining.
example 3:
var = 0
def func():
print var
var = 1
>>> func()
Traceback (most recent call last):
File "<pyshell#102>", line 1, in <module>
func()
File "C:/Users/aa/Desktop/test2.py", line 25, in func
print var
UnboundLocalError: local variable 'var' referenced before assignment
We expect with a dynamic language such as python that everything is
resolved dynamically. But this is not the case for functions. Local
variables are determined at compile time. PEP 227 and
http://docs.python.org/2.7/reference/executionmodel.html describe this
behavior this way
"If a name binding operation occurs anywhere within a code block, all
uses of the name within the block are treated as references to the
current block."
example 4:
x = "x in module"
class A():
print "A: " + x
x = "x in A"
print "A: " + x
print locals()
del x
print locals()
print "A: " + x
>>>
A: x in module
A: x in A
{'x': 'x in A', '__module__': '__main__'}
{'__module__': '__main__'}
A: x in module
But we see here that this statement in PEP227 "If a name binding
operation occurs anywhere within a code block, all uses of the name
within the block are treated as references to the current block." is
wrong when the code block is a class. Moreover, for classes, it seems
that local name binding is not made at compile time, but during
execution using the class namespace. In that respect,
PEP227 and the execution model in the Python doc is misleading and for
some parts wrong.
example 5:
x = 'x in module'
def f2():
x = 'x in f2'
def myfunc():
x = 'x in myfunc'
class MyClass(object):
x = x
print x
return MyClass
myfunc()
f2()
>>>
x in module
my understanding of this code is the following. The instruction x = x
first look up the object the right hand x of the expression is referring
to. In that case, the object is looked up locally in the class, then
following the rule LGB it is looked up in the global scope, which is
the string 'x in module'. Then a local attribute x to MyClass is
created in the class dictionary and pointed to the string object.
example 6:
Now here is an example I cannot explain.
It is very close to example 5, I am just changing the local MyClass
attribute from x to y.
x = 'x in module'
def f2():
x = 'x in f2'
def myfunc():
x = 'x in myfunc'
class MyClass(object):
y = x
print y
return MyClass
myfunc()
f2()
>>>
x in myfunc
Why in that case the x reference in MyClass is looked up in the
innermost function?

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 a interpreter loop. The 'regular' opcode for accessing a name is LOAD_NAME, which looks up a variable name as you would in a dictionary. LOAD_NAME will first look up a name as a local, and if that fails, looks for a global. LOAD_NAME throws a NameError exception when the name is not found.
For nested scopes, looking up names outside of the current scope is implemented using closures; if a name is not assigned to but is available in a nested (not global) scope, then such values are handled as a closure. This is needed because a parent scope can hold different values for a given name at different times; two calls to a parent function can lead to different closure values. So Python has LOAD_CLOSURE, MAKE_CLOSURE and LOAD_DEREF opcodes for that situation; the first two opcodes are used in loading and creating a closure for a nested scope, and the LOAD_DEREF will load the closed-over value when the nested scope needs it.
Now, LOAD_NAME is relatively slow; it will consult two dictionaries, which means it has to hash the key first and run a few equality tests (if the name wasn't interned). If the name isn't local, then it has to do this again for a global. For functions, that can potentially be called tens of thousands of times, this can get tedious fast. So function locals have special opcodes. Loading a local name is implemented by LOAD_FAST, which looks up local variables by index in a special local names array. This is much faster, but it does require that the compiler first has to see if a name is a local and not global. To still be able to look up global names, another opcode LOAD_GLOBAL is used. The compiler explicitly optimizes for this case to generate the special opcodes. LOAD_FAST will throw an UnboundLocalError exception when there is not yet a value for the name.
Class definition bodies on the other hand, although they are treated much like a function, do not get this optimization step. Class definitions are not meant to be called all that often; most modules create classes once, when imported. Class scopes don't count when nesting either, so the rules are simpler. As a result, class definition bodies do not act like functions when you start mixing scopes up a little.
So, for non-function scopes, LOAD_NAME and LOAD_DEREF are used for locals and globals, and for closures, respectively. For functions, LOAD_FAST, LOAD_GLOBAL and LOAD_DEREF are used instead.
Note that class bodies are executed as soon as Python executes the class line! So in example 1, class B inside class A is executed as soon as class A is executed, which is when you import the module. In example 2, C is not executed until f() is called, not before.
Lets walk through your examples:
You have nested a class A.B in a class A. Class bodies do not form nested scopes, so even though the A.B class body is executed when class A is executed, the compiler will use LOAD_NAME to look up x. A.B().f() is a function (bound to the B() instance as a method), so it uses LOAD_GLOBAL to load x. We'll ignore attribute access here, that's a very well defined name pattern.
Here f().C.z is at class scope, so the function f().C().g() will skip the C scope and look at the f() scope instead, using LOAD_DEREF.
Here var was determined to be a local by the compiler because you assign to it within the scope. Functions are optimized, so LOAD_FAST is used to look up the local and an exception is thrown.
Now things get a little weird. class A is executed at class scope, so LOAD_NAME is being used. A.x was deleted from the locals dictionary for the scope, so the second access to x results in the global x being found instead; LOAD_NAME looked for a local first and didn't find it there, falling back to the global lookup.
Yes, this appears inconsistent with the documentation. Python-the-language and CPython-the implementation are clashing a little here. You are, however, pushing the boundaries of what is possible and practical in a dynamic language; checking if x should have been a local in LOAD_NAME would be possible but takes precious execution time for a corner case that most developers will never run into.
Now you are confusing the compiler. You used x = x in the class scope, and thus you are setting a local from a name outside of the scope. The compiler finds x is a local here (you assign to it), so it never considers that it could also be a scoped name. The compiler uses LOAD_NAME for all references to x in this scope, because this is not an optimized function body.
When executing the class definition, x = x first requires you to look up x, so it uses LOAD_NAME to do so. No x is defined, LOAD_NAME doesn't find a local, so the global x is found. The resulting value is stored as a local, which happens to be named x as well. print x uses LOAD_NAME again, and now finds the new local x value.
Here you did not confuse the compiler. You are creating a local y, x is not local, so the compiler recognizes it as a scoped name from parent function f2().myfunc(). x is looked up with LOAD_DEREF from the closure, and stored in y.
You could see the confusion between 5 and 6 as a bug, albeit one that is not worth fixing in my opinion. It was certainly filed as such, see issue 532860 in the Python bug tracker, it has been there for over 10 years now.
The compiler could check for a scoped name x even when x is also a local, for that first assignment in example 5. Or LOAD_NAME could check if the name is meant to be a local, really, and throw an UnboundLocalError if no local was found, at the expense of more performance. Had this been in a function scope, LOAD_FAST would have been used for example 5, and an UnboundLocalError would be thrown immediately.
However, as the referenced bug shows, for historical reasons the behaviour is retained. There probably is code out there today that'll break were this bug fixed.

In two words, the difference between example 5 and example 6 is that in example 5 the variable x is also assigned to in the same scope, while not in example 6. This triggers a difference that can be understood by historical reasons.
This raises UnboundLocalError:
x = "foo"
def f():
print x
x = 5
f()
instead of printing "foo". It makes a bit of sense, even if it seems strange at first: the function f() defines the variable x locally, even if it is after the print, and so any reference to x in the same function must be to that local variable. At least it makes sense in that it avoids strange surprizes if you have by mistake reused the name of a global variable locally, and are trying to use both the global and the local variable. This is a good idea because it means that we can statically know, just by looking at a variable, which variable it means. For example, we know that print x refers to the local variable (and thus may raise UnboundLocalError) here:
x = "foo"
def f():
if some_condition:
x = 42
print x
f()
Now, this rule doesn't work for class-level scopes: there, we want expressions like x = x to work, capturing the global variable x into the class-level scope. This means that class-level scopes don't follow the basic rule above: we can't know if x in this scope refers to some outer variable or to the locally-defined x --- for example:
class X:
x = x # we want to read the global x and assign it locally
bar = x # but here we want to read the local x of the previous line
class Y:
if some_condition:
x = 42
print x # may refer to either the local x, or some global x
class Z:
for i in range(2):
print x # prints the global x the 1st time, and 42 the 2nd time
x = 42
So in class scopes, a different rule is used: where it would normally raise UnboundLocalError --- and only in that case --- it instead looks up in the module globals. That's all: it doesn't follow the chain of nested scopes.
Why not? I actually doubt there is a better explanation that "for historical reasons". In more technical terms, it could consider that the variable x is both locally defined in the class scope (because it is assigned to) and should be passed in from the parent scope as a lexically nested variable (because it is read). It would be possible to implement it by using a different bytecode than LOAD_NAME that looks up in the local scope, and falls back to using the nested scope's reference if not found.
EDIT: thanks wilberforce for the reference to http://bugs.python.org/issue532860. We may have a chance to get some discussion reactivated with the proposed new bytecode, if we feel that it should be fixed after all (the bug report considers killing support for x = x but was closed for fear of breaking too much existing code; instead what I'm suggesting here would be to make x = x work in more cases). Or I may be missing another fine point...
EDIT2: it seems that CPython did precisely that in the current 3.4 trunk: http://bugs.python.org/issue17853 ... or not? They introduced the bytecode for a slightly different reason and don't use it systematically...

Long story short, this is a corner case of Python's scoping that is a bit inconsistent, but has to be kept for backwards compatibility (and because it's not that clear what the right answer should be). You can see lots of the original discussion about it on the Python mailing list when PEP 227 was being implemented, and some in the bug for which this behaviour is the fix.
We can work out why there's a difference using the dis module, which lets us look inside code objects to see the bytecode a piece of code has been compiled to. I'm on Python 2.6, so the details of this might be slightly different - but I see the same behaviour, so I think it's probably close enough to 2.7.
The code that initialises each nested MyClass lives in a code object that you can get to via the attributes of the top-level functions. (I'm renaming the functions from example 5 and example 6 to f1 and f2 respectively.)
The code object has a co_consts tuple, which contains the myfunc code object, which in turn has the code that runs when MyClass gets created:
In [20]: f1.func_code.co_consts
Out[20]: (None,
'x in f2',
<code object myfunc at 0x1773e40, file "<ipython-input-3-6d9550a9ea41>", line 4>)
In [21]: myfunc1_code = f1.func_code.co_consts[2]
In [22]: MyClass1_code = myfunc1_code.co_consts[3]
In [23]: myfunc2_code = f2.func_code.co_consts[2]
In [24]: MyClass2_code = myfunc2_code.co_consts[3]
Then you can see the difference between them in bytecode using dis.dis:
In [25]: from dis import dis
In [26]: dis(MyClass1_code)
6 0 LOAD_NAME 0 (__name__)
3 STORE_NAME 1 (__module__)
7 6 LOAD_NAME 2 (x)
9 STORE_NAME 2 (x)
8 12 LOAD_NAME 2 (x)
15 PRINT_ITEM
16 PRINT_NEWLINE
17 LOAD_LOCALS
18 RETURN_VALUE
In [27]: dis(MyClass2_code)
6 0 LOAD_NAME 0 (__name__)
3 STORE_NAME 1 (__module__)
7 6 LOAD_DEREF 0 (x)
9 STORE_NAME 2 (y)
8 12 LOAD_NAME 2 (y)
15 PRINT_ITEM
16 PRINT_NEWLINE
17 LOAD_LOCALS
18 RETURN_VALUE
So the only difference is that in MyClass1, x is loaded using the LOAD_NAME op, while in MyClass2, it's loaded using LOAD_DEREF. LOAD_DEREF looks up a name in an enclosing scope, so it gets 'x in myfunc'. LOAD_NAME doesn't follow nested scopes - since it can't see the x names bound in myfunc or f1, it gets the module-level binding.
Then the question is, why does the code of the two versions of MyClass get compiled to two different opcodes? In f1 the binding is shadowing x in the class scope, while in f2 it's binding a new name. If the MyClass scopes were nested functions instead of classes, the y = x line in f2 would be compiled the same, but the x = x in f1 would be a LOAD_FAST - this is because the compiler would know that x is bound in the function, so it should use the LOAD_FAST to retrieve a local variable. This would fail with an UnboundLocalError when it was called.
In [28]: x = 'x in module'
def f3():
x = 'x in f2'
def myfunc():
x = 'x in myfunc'
def MyFunc():
x = x
print x
return MyFunc()
myfunc()
f3()
---------------------------------------------------------------------------
Traceback (most recent call last)
<ipython-input-29-9f04105d64cc> in <module>()
9 return MyFunc()
10 myfunc()
---> 11 f3()
<ipython-input-29-9f04105d64cc> in f3()
8 print x
9 return MyFunc()
---> 10 myfunc()
11 f3()
<ipython-input-29-9f04105d64cc> in myfunc()
7 x = x
8 print x
----> 9 return MyFunc()
10 myfunc()
11 f3()
<ipython-input-29-9f04105d64cc> in MyFunc()
5 x = 'x in myfunc'
6 def MyFunc():
----> 7 x = x
8 print x
9 return MyFunc()
UnboundLocalError: local variable 'x' referenced before assignment
This fails because the MyFunc function then uses LOAD_FAST:
In [31]: myfunc_code = f3.func_code.co_consts[2]
MyFunc_code = myfunc_code.co_consts[2]
In [33]: dis(MyFunc_code)
7 0 LOAD_FAST 0 (x)
3 STORE_FAST 0 (x)
8 6 LOAD_FAST 0 (x)
9 PRINT_ITEM
10 PRINT_NEWLINE
11 LOAD_CONST 0 (None)
14 RETURN_VALUE
(As an aside, it's not a big surprise that there should be a difference in how scoping interacts with code in the body of classes and code in a function. You can tell this because bindings at the class level aren't available in methods - method scopes aren't nested inside the class scope in the same way as nested functions are. You have to explicitly reach them via the class, or by using self. (which will fall back to the class if there's not also an instance-level binding).)

Double underscore for Python method *argument*

I know what double underscore means for Python class attributes/methods, but does it mean something for method argument?
It looks like you cannot pass argument starting with double underscore to methods. It is confusing because you can do that for normal functions.
Consider this script:
def egg(__a=None):
return __a
print "egg(1) =",
print egg(1)
print
class Spam(object):
def egg(self, __a=None):
return __a
print "Spam().egg(__a=1) =",
print Spam().egg(__a=1)
Running this script yields:
egg(1) = 1
Spam().egg(__a=1) =
Traceback (most recent call last):
File "/....py", line 15, in <module>
print Spam().egg(__a=1)
TypeError: egg() got an unexpected keyword argument '__a'
I checked this with Python 2.7.2.
Some other examples
This works:
def egg(self, __a):
return __a
class Spam(object):
egg = egg
Spam().egg(__a=1)
This does not:
class Spam(object):
def _egg(self, __a=None):
return __a
def egg(self, a):
return self._egg(__a=a)
Spam().egg(1)

Name mangling applies to all identifiers with leading double underscores, regardless of where they occur (second to last sentence in that section):
This transformation is independent of the syntactical context in which the identifier is used.
This is simpler to implement and to define, and more consistent. It may seem stupid, but the whole name mangling deal is an ugly little hack IMHO; and you're not expected to use names like that for anything except attributes/methods anyway.
Spam().egg(_Spam__a=1), as well as Spam().egg(1), does work. But even though you can make it work, leading underscores (any number of them) have no place in parameter names. Or in any local variable (exception: _) for that matter.
Edit: You appear to have found a corner case nobody ever considered. The documentation is imprecise here, or the implementation is flawed. It appears keyword argument names are not mangled. Look at the bytecode (Python 3.2):
>>> dis.dis(Spam.egg)
3 0 LOAD_FAST 0 (self)
3 LOAD_ATTR 0 (_egg)
6 LOAD_CONST 1 ('__a') # keyword argument not mangled
9 LOAD_FAST 1 (a)
12 CALL_FUNCTION 256
15 RETURN_VALUE
>>> dis.dis(Spam._egg)
2 0 LOAD_FAST 1 (_Spam__a) # parameter mangled
3 RETURN_VALUE
This may be rooted in the fact that keyword arguments are equivalent to passing a dict (in this case {'__a': 1}) whose keys wouldn't be mangled either. But honestly, I'd just call it an ugly corner case in an already ugly special case and move on. It's not important because you shouldn't use identifiers like that anyway.

It gets converted to _Spam__a:
In [20]: class Spam(object):
....:
....: def egg(self, __a=None):
....: return __a
....:
In [21]: Spam.egg.__func__.__code__.co_varnames
Out[21]: ('self', '_Spam__a')

Double Underscore or Name Mangling in a class context is a private identifier. In your example try dir(Spam.egg) and you will see that the parameter __a is now _Spam__egg.
You can now use:
Spam().egg(_Spam__a=1)

How to find the name of a variable that was passed to a function?

In C/C++, I have often found it useful while debugging to define a macro, say ECHO(x), that prints out the variable name and its value (i.e. ECHO(variable) might print variable 7). You can get the variable name in a macro using the 'stringification' operator # as described here. Is there a way of doing this in Python?
In other words, I would like a function
def echo(x):
#magic goes here
which, if called as foo=7; echo(foo) (or foo=7; echo('foo'), maybe), would print out foo 7. I realise it is trivial to do this if I pass both the variable and its name to the function, but I use functions like this a lot while debugging, and the repetition always ends up irritating me.

Not really solution, but may be handy (anyway you have echo('foo') in question):
def echo(**kwargs):
for name, value in kwargs.items():
print name, value
foo = 7
echo(foo=foo)
UPDATE: Solution for echo(foo) with inspect
import inspect
import re
def echo(arg):
frame = inspect.currentframe()
try:
context = inspect.getframeinfo(frame.f_back).code_context
caller_lines = ''.join([line.strip() for line in context])
m = re.search(r'echo\s*\((.+?)\)$', caller_lines)
if m:
caller_lines = m.group(1)
print caller_lines, arg
finally:
del frame
foo = 7
bar = 3
baz = 11
echo(foo)
echo(foo + bar)
echo((foo + bar)*baz/(bar+foo))
Output:
foo 7
foo + bar 10
(foo + bar)*baz/(bar+foo) 11
It has the smallest call, but it's sensitive to newlines, e.g.:
echo((foo + bar)*
baz/(bar+foo))
Will print:
baz/(bar+foo)) 11

def echo(x):
import inspect
print "{0}: {1}".format(x, inspect.stack()[1][0].f_locals[x])
y = 123
echo('y')
# 'y: 123'
See also: https://stackoverflow.com/a/2387854/16361
Note that this can cause GC issues:
http://docs.python.org/library/inspect.html#the-interpreter-stack
It will also turn off people who have been burned by messing with frames, and may leave a bad taste in your mouth. But it will work.

Here's a solution that has you type a bit more to call it. It relies on the locals built-in function:
def print_key(dictionary, key):
print key, '=', dictionary[key]
foo = 7
print_key(locals(), 'foo')
An echo with the semantics you mentioned is also possible, using the inspect module. However, do read the warnings in inspect's documentation. This is an ugly non-portable hack (it doesn't work in all implementations of Python). Be sure to only use it for debugging.
The idea is to look into the locals of the calling function. The inspect module allows just that: calls are represented by frame objects linked together by the f_back attribute. Each frame's local and global variables are available (there are also builtins, but you're unlikely to need to print them).
You may want to explicitly delete any references frame objects to prevent reference cycles, as explained in inspect docs, but this is not strictly necessary – the garbage collection will free them sooner or later.
import inspect
def echo(varname):
caller = inspect.currentframe().f_back
try:
value = caller.f_locals[varname]
except KeyError:
value = caller.f_globals[varname]
print varname, '=', value
del caller
foo = 7
echo('foo')

Python 3.2.1: exec('x = y()') sets a value in a toy example, but not in the full code

I'm using an exec() statement to set a value, like so:
foo = 3
def return_4():
return 4
instruction = 'foo = return_4()'
exec(instruction) # <---- WHERE THE MAGIC HAPPENS
print(foo)
This comes out as 4, as I expect.
My program has operations for manipulating a Rubik's cube. In this stripped down version, I'll do four things:
I'll instantiate a cube, filling in one face (with abbreviations for 'front top left' and 'front bottom right' and the like).
I'll have a function that rotates that front face.
I'll have an 'interpreter' function, which takes a cube and a list of instructions, and applies those instructions to the cube, returning the modified cube. Here's where I use 'exec' (and where I think the breakage happens).
Finally, I'll run the interpreter on my partial cube with the instruction to rotate the face one time.
+
my_cube = [['FTL', 'FTM', 'FTR',
'FML', 'FMM', 'FMR',
'FBL', 'FBM', 'FBR'],
[],[],[],[],[]] # other faces specified in actual code
def rotate_front(cube):
front = cube[0]
new_front = [front[6],front[3],front[0],
front[7],front[4],front[1],
front[8],front[5],front[2]]
# ...
ret_cube = cube
ret_cube[0] = new_front
# pdb says we are returning a correctly rotated cube,
# and calling this directly returns the rotated cube
return ret_cube
def process_algorithm(cube=default_cube, algorithm=[]):
return_cube = cube
for instruction in algorithm:
exec('return_cube = ' + instruction + '(return_cube)') # <--- NO MAGIC!
# ACCORDING TO pdb, return_cube HAS NOT BEEN ROTATED!
return return_cube
process_algorithm(cube = my_cube, algorithm = ['rotate_front'])
If I replace the exec(x = y) formation with x = eval(y), it seems to work.
return_cube = eval(instruction + '(return_cube)')
So maybe this is just academic. Why does the toy example work, and the actual code fail?
(Am I doing something obvious and silly, like missing an equals sign? I'm going to kick myself, I bet...)
Thanks for any help anyone can offer.

On Python 2.x, exec was a statement that changed variable lookup from LOAD_GLOBAL and LOAD_FAST to LOAD_NAME to every name you access on your function.
That means it first search the local scope to see if can find the name to after check the global scope.
Now, on Python 3.x, the exec function cannot change this lookup and will never find the name you defined unless you add an argument with the
scope you want the result to be evaluated.
exec(some_code, globals())
For that to work, you need to add global my_var inside the function to make sure the lookup will work.
Keep in mind these things will be inserted in the global namespace of your module...
BTW, why do you need exec or eval? Why can't you add real functions to your algorithm list?
As side note, I can see you don't change the algorithm var on your function, but if you do it'll introduce some undesired side effects because the default value you created is mutable and will be used on all function calls.
For safety, change it to None and create a new list if needed.

This doesn't attempt to answer the question, but rather expands the test-cases so the behavior can be seen better and preserved for reference. The results come from Python 3.2.2 on Windows. See JBernardo's answer for a "why" this behavior occurs.
From global scope ("toy example"):
>>> foo = "global-foo"
>>> exec('foo = "global-bar"')
>>> foo
'global-bar'
In a function (full context):
>>> def setIt():
foo = "local-foo"
exec('foo = "local-bar"')
return foo
foo = "global-foo"
>>> setIt()
'local-foo'
>>> foo
'global-foo'
With specifying globals() (does not work for locals() at all):
>>> def setIt():
foo = "local-foo"
exec('foo = "local-bar"', globals())
return foo
>>> foo = "global-foo"
>>> setIt()
'local-foo'
>>> foo
'local-bar'
Happy coding.