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Python Interview Questions

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Did you do something like this?

x = 1 # make a global
def f():
    print x # try to print the global
    ...
    for j in range(100):
        if q>3:
            x=4

Any variable assigned in a function is local to that function. unless it is specifically declared global. Since a value is bound to x as the last statement of the function body, the compiler assumes that x is local. Consequently the print x attempts to print an uninitialized local variable and will trigger a NameError.
The solution is to insert an explicit global declaration at the start of the function:

def f():
    global x
    print x # try to print the global
    ...
    for j in range(100):
        if q>3:
            x=4

In this case, all references to x are interpreted as references to the x from the module namespace. 

In Python, variables that are only referenced inside a function are implicitly global. If a variable is assigned a new value anywhere within the function's body, it's assumed to be a local. If a variable is ever assigned a new value inside the function, the variable is implicitly local, and you need to explicitly declare it as 'global'.
Though a bit surprising at first, a moment's consideration explains this. On one hand, requiring global for assigned variables provides a bar against unintended side-effects. On the other hand, if global was required for all global references, you'd be using global all the time. You'd have to declare as global every reference to a builtin function or to a component of an imported module. This clutter would defeat the usefulness of the global declaration for identifying side-effects. 

The canonical way to share information across modules within a single program is to create a special module (often called config or cfg). Just import the config module in all modules of your application; the module then becomes available as a global name. Because there is only one instance of each module, any changes made to the module object get reflected everywhere. For example:

config.py:
x = 0 # Default value of the 'x' configuration setting
mod.py:
import config
config.x = 1

main.py:
import config
import mod
print config.x

Note that using a module is also the basis for implementing the Singleton design pattern, for the same reason.

Collect the arguments using the * and ** specifier in the function's parameter list; this gives you the positional arguments as a tuple and the keyword arguments as a dictionary. You can then pass these arguments when calling another function by using * and **:

def f(x, *tup, **kwargs):
...
kwargs['width']='14.3c'
...
g(x, *tup, **kwargs)

In the unlikely case that you care about Python versions older than 2.0, use 'apply':

def f(x, *tup, **kwargs):
...
kwargs['width']='14.3c'
...
apply(g, (x,)+tup, kwargs)

You have two choices: you can use nested scopes or you can use callable objects. For example, suppose you wanted to define linear(a,b) which returns a function f(x) that computes the value a*x+b. Using nested scopes:

def linear(a,b):
def result(x):
return a*x + b
return result

Or using a callable object:

class linear:
def __init__(self, a, b):
self.a, self.b = a,b
def __call__(self, x):
return self.a * x + self.b

In both cases:

taxes = linear(0.3,2)

gives a callable object where taxes(10e6) == 0.3 * 10e6 + 2.

The callable object approach has the disadvantage that it is a bit slower and results in slightly longer code. However, note that a collection of callables can share their signature via inheritance:

class exponential(linear):
# __init__ inherited
def __call__(self, x):
return self.a * (x ** self.b)

Object can encapsulate state for several methods:

class counter:
value = 0
def set(self, x): self.value = x
def up(self): self.value=self.value+1
def down(self): self.value=self.value-1

count = counter()
inc, dec, reset = count.up, count.down, count.set

Here inc(), dec() and reset() act like functions which share the same counting variable.

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