Local Variables in Nested Functions

Python nested functions variable scoping

When I run your code I get this error:

UnboundLocalError: local variable '_total' referenced before assignment

This problem is caused by this line:

_total += PRICE_RANGES[key][0]

The documentation about Scopes and Namespaces says this:

A special quirk of Python is that – if no global statement is in effect – assignments to names always go into the innermost scope. Assignments do not copy data — they just bind names to objects.

So since the line is effectively saying:

_total = _total + PRICE_RANGES[key][0]

it creates _total in the namespace of recurse(). Since _total is then new and unassigned you can't use it in the addition.

Access variable from function inside of nested function

You have two options:

Give $content as a parameter

function foo() {
  $content = 'foobar';

  function bar($content) {
      echo $content; // echos something
    }
}

Take $content outside of the function and use global there.

$content = '';

function foo() {
    global $content;

    $content .= 'foobar';

    function bar($content) {
        global $content;

        echo $content; // echos something
    }
}

Variable scope in Python nested function

It seems this is a commonly asked question as stated in this link. The reason is that variable a inside swim becomes a local variable as soon as there is an assignment to a. It shadows the external a, and local a is not defined before assignment in function swim, so the error rises.

Thanks for all your guys' answers!

Using 'get' Inside Nested Functions on Local Variables

Since you are asking, this definitely looks like a bad design to me. The recommended approach is to stick to R's way of pass-by-value. And as much as possible, make every function take everything it uses as arguments:

test1 <- function(a1, b1, c1 = 1) {
   testvalue <- c1 * a1 * b1   
   testvalue
}

test2 <- function(cc = 1) {
   a <- 1
   b <- 2
   test1(a1 = a, b1 = b, c1 = cc)
}

cc <- 3
test2(cc = cc)

(I replaced c with cc since it is the name of a function, hence a bad idea to use as variable name.)

A less acceptable but maybe closer approach to what you have is to not pass all arguments to your functions and let R look for them in the calling stack:

test1 <- function(a1, b1) {
   testvalue <- cc * a1 * b1   
   testvalue
}

test2 <- function() {
   a <- 1
   b <- 2
   test1(a, b)
}

cc <- 3
test2()

If for some reason the first approach does not work for you, please explain why so I get a chance to maybe convince you otherwise. It is the recommended way of programming in R.

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Following on the discussion and your edit, I'll recommend you look at the proto package as an alternative to get and assign. Essentially, proto objects are environments so it's nothing you can't do with base R but it helps make things a bit cleaner:

test1 <- function(x) {
   testvalue <- x$c * x$a * x$b
   x$a <- 3.5
   testvalue
}

test2 <- function(x) {
   x$a <- 1
   x$b <- 2
   cat(x$a, '\n')
   test1(x)
   cat(x$a, '\n')
}

library(proto)
x <- proto(c = 3)
test2(x)

From a programming point of view, test1 and test2 are functions with side-effects (they modify the object x). Beware that its a risky practice.

Or maybe a better approach is to make test1 and test2 be methods of a class, then it is acceptable if they modify the instance they are running on:

x <- proto() # defines a class

x$test1 <- function(.) {
   testvalue <- .$c * .$a * .$b
   .$a <- 3.5
   testvalue
}

x$test2 <- function(.) {
   .$a <- 1
   .$b <- 2
   cat(.$a, '\n')
   .$test1()
   cat(.$a, '\n')
}

library(proto)
y <- x$proto(c = 3)  # an instance of the class
y$test2()

If you are not interested in using a third-party package (proto), then look at R's support for building classes (setClass, setRefClass). I do believe using an object-oriented design is the right approach given your specs.

Using Global Variables inside a Nested Function in Python

In add, x is not a global variable; it's local to add. You either need to make it global as well, so that add and change are referring to the same variable

def add(): 
    global x
    x = 15
    
    def change(): 
        global x 
        x = 20
    print("Before making changes: ", x) 
    print("Making change") 
    change() 
    print("After making change: ", x) 

add() 
print("value of x",x)

or you need to declare x in change as nonlocal, rather than global.

def add(): 
    x = 15
    
    def change(): 
        nonlocal x 
        x = 20
    print("Before making changes: ", x) 
    print("Making change") 
    change() 
    print("After making change: ", x) 

add() 
print("value of x",x)

Local variables in nested functions

The nested function looks up variables from the parent scope when executed, not when defined.

The function body is compiled, and the 'free' variables (not defined in the function itself by assignment), are verified, then bound as closure cells to the function, with the code using an index to reference each cell. pet_function thus has one free variable (cage) which is then referenced via a closure cell, index 0. The closure itself points to the local variable cage in the get_petters function.

When you actually call the function, that closure is then used to look at the value of cage in the surrounding scope at the time you call the function. Here lies the problem. By the time you call your functions, the get_petters function is already done computing it's results. The cage local variable at some point during that execution was assigned each of the 'cow', 'dog', and 'cat' strings, but at the end of the function, cage contains that last value 'cat'. Thus, when you call each of the dynamically returned functions, you get the value 'cat' printed.

The work-around is to not rely on closures. You can use a partial function instead, create a new function scope, or bind the variable as a default value for a keyword parameter.

• Partial function example, using functools.partial():

  from functools import partial
  
  def pet_function(cage=None):
      print "Mary pets the " + cage.animal + "."
  
  yield (animal, partial(gotimes, partial(pet_function, cage=cage)))
  

• Creating a new scope example:

  def scoped_cage(cage=None):
      def pet_function():
          print "Mary pets the " + cage.animal + "."
      return pet_function
  
  yield (animal, partial(gotimes, scoped_cage(cage)))
  

• Binding the variable as a default value for a keyword parameter:

  def pet_function(cage=cage):
      print "Mary pets the " + cage.animal + "."
  
  yield (animal, partial(gotimes, pet_function))
  

There is no need to define the scoped_cage function in the loop, compilation only takes place once, not on each iteration of the loop.

Why nested functions can access variables from outer functions, but are not allowed to modify them

def f1():
    x = { 'value': 5 }
    def f2():
        x['value'] += 1

Workaround is to use a mutable object and update members of that object. Name binding is tricky in Python, sometimes.

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