Lambda function behavior with and without keyword arguments

Question

I am using lambda functions for GUI programming with tkinter.
Recently I got stuck when implementing buttons that open files:

self.file=""
button = Button(conf_f, text="Tools opt.",
        command=lambda: tktb.helpers.openfile(self.file))

As you see, I want to define a file path that can be updated, and that is not known when creating the GUI.
The issue I had is that earlier my code was :

button = Button(conf_f, text="Tools opt.",
        command=lambda f=self.file: tktb.helpers.openfile(f))

The lambda function had a keyword argument to pass the file path. In this case, the parameter f was not updated when self.file was.

I got the keyword argument from a code snippet and I use it everywhere. Obviously I shouldn’t…

This is still not clear to me… Could someone explain me the difference between the two lambda forms and when to use one an another?

PS: The following comment led me to the solution but I’d like a little more explanations:
lambda working oddly with tkinter

Asked By: Plouff

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Source

Answer 1

I’ll try to explain it more in depth.

If you do

i = 0
f = lambda: i

you create a function (lambda is essentially a function) which accesses its enclosing scope’s i variable.

Internally, it does so by having a so-called closure which contains the i. It is, loosely spoken, a kind of pointer to the real variable which can hold different values at different points of time.

def a():
    # first, yield a function to access i
    yield lambda: i
    # now, set i to different values successively
    for i in range(100): yield

g = a() # create generator
f = next(g) # get the function
f() # -> error as i is not set yet
next(g)
f() # -> 0
next(g)
f() # -> 1
# and so on
f.func_closure # -> an object stemming from the local scope of a()
f.func_closure[0].cell_contents # -> the current value of this variable

Here, all values of i are – at their time – stored in that said closure. If the function f() needs them. it gets them from there.

You can see that difference on the disassembly listings:

These said functions a() and f() disassemble like this:

>>> dis.dis(a)
  2           0 LOAD_CLOSURE             0 (i)
              3 BUILD_TUPLE              1
              6 LOAD_CONST               1 (<code object <lambda> at 0xb72ea650, file "<stdin>", line 2>)
              9 MAKE_CLOSURE             0
             12 YIELD_VALUE
             13 POP_TOP

  3          14 SETUP_LOOP              25 (to 42)
             17 LOAD_GLOBAL              0 (range)
             20 LOAD_CONST               2 (100)
             23 CALL_FUNCTION            1
             26 GET_ITER
        >>   27 FOR_ITER                11 (to 41)
             30 STORE_DEREF              0 (i)
             33 LOAD_CONST               0 (None)
             36 YIELD_VALUE
             37 POP_TOP
             38 JUMP_ABSOLUTE           27
        >>   41 POP_BLOCK
        >>   42 LOAD_CONST               0 (None)
             45 RETURN_VALUE
>>> dis.dis(f)
  2           0 LOAD_DEREF               0 (i)
              3 RETURN_VALUE

Compare that to a function b() which looks like

>>> def b():
...   for i in range(100): yield
>>> dis.dis(b)
  2           0 SETUP_LOOP              25 (to 28)
              3 LOAD_GLOBAL              0 (range)
              6 LOAD_CONST               1 (100)
              9 CALL_FUNCTION            1
             12 GET_ITER
        >>   13 FOR_ITER                11 (to 27)
             16 STORE_FAST               0 (i)
             19 LOAD_CONST               0 (None)
             22 YIELD_VALUE
             23 POP_TOP
             24 JUMP_ABSOLUTE           13
        >>   27 POP_BLOCK
        >>   28 LOAD_CONST               0 (None)
             31 RETURN_VALUE

The main difference in the loop is

        >>   13 FOR_ITER                11 (to 27)
             16 STORE_FAST               0 (i)

in b() vs.

        >>   27 FOR_ITER                11 (to 41)
             30 STORE_DEREF              0 (i)

in a(): the STORE_DEREF stores in a cell object (closure), while STORE_FAST uses a “normal” variable, which (probably) works a little bit faster.

The lambda as well makes a difference:

>>> dis.dis(lambda: i)
  1           0 LOAD_GLOBAL              0 (i)
              3 RETURN_VALUE

Here you have a LOAD_GLOBAL, while the one above uses LOAD_DEREF. The latter, as well, is for the closure.

I completely forgot about lambda i=i: i.

If you have the value as a default parameter, it finds its way into the function via a completely different path: the current value of i gets passed to the just created function via a default parameter:

>>> i = 42
>>> f = lambda i=i: i
>>> dis.dis(f)
  1           0 LOAD_FAST                0 (i)
              3 RETURN_VALUE

This way the function gets called as f(). It detects that there is a missing argument and fills the respective parameter with the default value. All this happens before the function is called; from within the function you just see that the value is taken and returned.

And there is yet another way to accomplish your task: Just use the lambda as if it would take a value: lambda i: i. If you call this, it complains about a missing argument.

But you can cope with that with the use of functools.partial:

ff = [functools.partial(lambda i: i, x) for x in range(100)]
ff[12]()
ff[54]()

This wrapper gets a callable and a number of arguments to be passed. The resulting object is a callable which calls the original callable with these arguments plus any arguments you give to it. It can be used here to keep locked to the value intended.

Answered By: glglgl

Lambda function behavior with and without keyword arguments

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