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Calling a string as a function using eval()

I wish to call append function for a list with append passed in as a string. For instance:
List = [1,2]
func = 'append'
value = 3
# I wish to call List.append(value)
I know there is a way using eval but, I am unable to figure it out.

You can use the getattr built-in function:
>>> my_list = [1, 2]
>>> func = 'append'
>>> value = 3
>>> getattr(my_list, func)(value)
>>> my_list
[1, 2, 3]

Use getattr.
List = [1,2]
func = 'append'
value = 3
f = getattr(List, func)
f(value)
print(List)

Deleting an default argument inside function in Python

I have tried the following in the console:
>>> def f(l=[]):
... l.append(1)
... print(l)
... del l
...
>>> f()
[1]
>>> f()
[1, 1]
What I don't understand is how the interpreter is still able to find the same list l after the delete instruction.
From the documentation l=[] should be evaluated only once.

The variable is not the object. Each time the function is called, the local variable l is created and (if necessary) set to the default value.
The object [], which is the default value for l, is created when the function is defined, but the variable l is created each time the function runs.

To delete a element for the list,del l[:] should be used.If u use just l the list will remain itself.
def f(l=[]):
l.append(1)
print(l)
del l[:]
print(l)
>>> f()
[1] #element in the list
[] #list after deletion of the element
>>> f()
[1]
[]
>>> f()
[1]
[]

What is the difference between del a_list[:] and a_list = [] in a function?

This is just a question asking for the difference in the code.
I have several lists ie. a=[], b=[], c=[], d=[]
Say if I have a code that appends to each list, and I want to reset all these lists to its original empty state, I created a function:
def reset_list():
del a[:]
del b[:]
del c[:]
del d[:]
So whenever I call reset_list() in a code, it removes all the appended items and set all lists to []. However, the one below doesn't work:
def reset_list():
a = []
b = []
c = []
d = []
This might be a stupid question but I was wondering why the second one wouldn't work.

When you do del a[:] then it looks for the variable a (including outer contexts) and then performs del found_a[:] on it.
But when you use a = [] it creates a name a in the current context and assigns an empty list to it. When the function exits the variable a from the function is not "accessible" anymore (destroyed).
So in short the first works because you change the a from an outer context, the second does not work because you don't modify the a from the outer context, you just create a new a name and temporarily (for the duration of the function) assigns an empty list to it.
There's a difference between del a[:] and a = []
Note that these actually do something different which becomes apparent if you have additional references (aliases) to the original list. (as noted by #juanpa.arrivillaga in the comments)
del list[:] deletes all elements in the list but doesn't create a new list, so the aliases are updated as well:
>>> list_1 = [1,2,3]
>>> alias_1 = list_1
>>> del alist_1[:]
>>> list_1
[]
>>> alias_1
[]
However a = [] creates a new list and assigns that to a:
>>> list_2 = [1,2,3]
>>> alias_2 = list_2
>>> list_2 = []
>>> list_2
[]
>>> alias_2
[1, 2, 3]
If you want a more extensive discussion about names and references in Python I can highly recommend Ned Batchelders blog post on "Facts and myths about Python names and values".
A better solution?
In most cases where you have multiple variables that belong together I would use a class for them. Then instead of reset you could simply create a new instance and work on that:
class FourLists:
def __init__(self):
self.a = []
self.b = []
self.c = []
self.d = []
Then you can create a new instance and work with the attributes of that instance:
>>> state = FourLists()
>>> state.a
[]
>>> state.b.append(10)
>>> state.b.extend([1,2,3])
>>> state.b
[10, 1, 2, 3]
Then if you want to reset the state you could simply create a new instance:
>>> new_state = FourLists()
>>> new_state.b
[]

You need to declare a,b,c,d as global if you want python to use the globally defined 'versions' of your variables. Otherwise, as pointed out in other answers, it will simply declare new local-scope 'versions'.
a = [1,2,3]
b = [1,2,3]
c = [1,2,3]
d = [1,2,3]
def reset_list():
global a,b,c,d
a = []
b = []
c = []
d = []
print(a,b,c,d)
reset_list()
print(a,b,c,d)
Outputs:
[1, 2, 3] [1, 2, 3] [1, 2, 3] [1, 2, 3]
[] [] [] []
As pointed out by #juanpa.arrivillaga, there is a difference between del a[:] and a = []. See this answer.

The 1st method works because:
reset_list() simply deletes the contents of the four lists. It works on the lists that you define outside the function, provided they are named the same. If you had a different name, you'd get an error:
e = [1,2,3,4]
def reset_list():
del a[:] #different name for list
NameError: name 'e' is not defined
The function will only have an effect if you initialize the lists before the function call. This is because you are not returning the lists back after the function call ends:
a = [1,2,3,4] #initialize before function definition
def reset_list():
del a[:]
reset_list() #function call to modify a
print(a)
#[]
By itself the function does not return anything:
print(reset_list())
#None
The 2nd method doesn't work because:
the reset_list() function creates 4 empty lists that are not pointing to the lists that may have been defined outside the function. Whatever happens inside the function stays inside(also called scope) and ends there unless you return the lists back at the end of the function call. The lists will be modified and returned only when the function is called. Make sure that you specify the arguments in reset_list(a,..) in the function definition:
#function definition
def reset_list(a):
a = []
return a
#initialize list after function call
a = [1,2,3,4]
print("Before function call:{}".format(a))
new_a = reset_list(a)
print("After function call:{}".format(new_a))
#Output:
Before function call:[1, 2, 3, 4]
After function call:[]
As you've seen, you should always return from a function to make sure that your function "does some work" on the lists and returns the result in the end.

The second function (with a = [ ] and so on) initialises 4 new lists with a local scope (within the function). It is not the same as deleting the contents of the list.

Copy values from one list to another without altering the reference in python

In python objects such as lists are passed by reference. Assignment with the = operator assigns by reference. So this function:
def modify_list(A):
A = [1,2,3,4]
Takes a reference to list and labels it A, but then sets the local variable A to a new reference; the list passed by the calling scope is not modified.
test = []
modify_list(test)
print(test)
prints []
However I could do this:
def modify_list(A):
A += [1,2,3,4]
test = []
modify_list(test)
print(test)
Prints [1,2,3,4]
How can I assign a list passed by reference to contain the values of another list? What I am looking for is something functionally equivelant to the following, but simpler:
def modify_list(A):
list_values = [1,2,3,4]
for i in range(min(len(A), len(list_values))):
A[i] = list_values[i]
for i in range(len(list_values), len(A)):
del A[i]
for i in range(len(A), len(list_values)):
A += [list_values[i]]
And yes, I know that this is not a good way to do <whatever I want to do>, I am just asking out of curiosity not necessity.

You can do a slice assignment:
>>> def mod_list(A, new_A):
... A[:]=new_A
...
>>> liA=[1,2,3]
>>> new=[3,4,5,6,7]
>>> mod_list(liA, new)
>>> liA
[3, 4, 5, 6, 7]

The simplest solution is to use:
def modify_list(A):
A[::] = [1, 2, 3, 4]

To overwrite the contents of a list with another list (or an arbitrary iterable), you can use the slice-assignment syntax:
A = B = [1,2,3]
A[:] = [4,5,6,7]
print(A) # [4,5,6,7]
print(A is B) # True
Slice assignment is implemented on most of the mutable built-in types. The above assignment is essentially the same the following:
A.__setitem__(slice(None, None, None), [4,5,6,7])
So the same magic function (__setitem__) is called when a regular item assignment happens, only that the item index is now a slice object, which represents the item range to be overwritten. Based on this example you can even support slice assignment in your own types.

Python why would you use [:] over =

I am just learning python and I am going though the tutorials on https://developers.google.com/edu/python/strings
Under the String Slices section
s[:] is 'Hello' -- omitting both always gives us a copy of the whole
thing (this is the pythonic way to copy a sequence like a string or
list)
Out of curiosity why wouldn't you just use an = operator?
s = 'hello';
bar = s[:]
foo = s
As far as I can tell both bar and foo have the same value.

= makes a reference, by using [:] you create a copy. For strings, which are immutable, this doesn't really matter, but for lists etc. it is crucial.
>>> s = 'hello'
>>> t1 = s
>>> t2 = s[:]
>>> print s, t1, t2
hello hello hello
>>> s = 'good bye'
>>> print s, t1, t2
good bye hello hello
but:
>>> li1 = [1,2]
>>> li = [1,2]
>>> li1 = li
>>> li2 = li[:]
>>> print li, li1, li2
[1, 2] [1, 2] [1, 2]
>>> li[0] = 0
>>> print li, li1, li2
[0, 2] [0, 2] [1, 2]
So why use it when dealing with strings? The built-in strings are immutable, but whenever you write a library function expecting a string, a user might give you something that "looks like a string" and "behaves like a string", but is a custom type. This type might be mutable, so it's better to take care of that.
Such a type might look like:
class MutableString(object):
def __init__(self, s):
self._characters = [c for c in s]
def __str__(self):
return "".join(self._characters)
def __repr__(self):
return "MutableString(\"%s\")" % str(self)
def __getattr__(self, name):
return str(self).__getattribute__(name)
def __len__(self):
return len(self._characters)
def __getitem__(self, index):
return self._characters[index]
def __setitem__(self, index, value):
self._characters[index] = value
def __getslice__(self, start, end=-1, stride=1):
return str(self)[start:end:stride]
if __name__ == "__main__":
m = MutableString("Hello")
print m
print len(m)
print m.find("o")
print m.find("x")
print m.replace("e", "a") #translate to german ;-)
print m
print m[3]
m[1] = "a"
print m
print m[:]
copy1 = m
copy2 = m[:]
print m, copy1, copy2
m[1] = "X"
print m, copy1, copy2
Disclaimer: This is just a sample to show how it could work and to motivate the use of [:]. It is untested, incomplete and probably horribly performant

They have the same value, but there is a fundamental difference when dealing with mutable objects.
Say foo = [1, 2, 3]. You assign bar = foo, and baz = foo[:]. Now let's say you want to change bar - bar.append(4). You check the value of foo, and...
print foo
# [1, 2, 3, 4]
Now where did that extra 4 come from? It's because you assigned bar to the identity of foo, so when you change one you change the other. You change baz - baz.append(5), but nothing has happened to the other two - that's because you assigned a copy of foo to baz.
Note however that because strings are immutable, it doesn't matter.

If you have a list the result is different:
l = [1,2,3]
l1 = l
l2 = l[:]
l2 is a copy of l (different object) while l1 is an alias of l which means that l1[0]=7 will modify also l, while l2[1]=7 will not modify l.

While referencing an object and referencing the object's copy doesn't differ for an immutable object like string, they do for mutable objects (and mutable methods), for instance list.
Same thing on mutable objects:
a = [1,2,3,4]
b = a
c = a[:]
a[0] = -1
print a # will print [1,2,3,4]
print b # will print [-1,2,3,4]
print c # will print [1,2,3,4]
A visualization on pythontutor of the above example - http://goo.gl/Aswnl.