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Interesting results with the '+=' increment operator [duplicate]

This question already has answers here:
Why does += behave unexpectedly on lists?
(9 answers)
Closed last month.
I had learned that n = n + v and n += v are the same. Until this;
def assign_value(n, v):
n += v
print(n)
l1 = [1, 2, 3]
l2 = [4, 5, 6]
assign_value(l1, l2)
print(l1)
The output will be:
[1, 2, 3, 4, 5, 6]
[1, 2, 3, 4, 5, 6]
Now when I use the expanded version:
def assign_value(n, v):
n = n + v
print(n)
l1 = [1, 2, 3]
l2 = [4, 5, 6]
assign_value(l1, l2)
print(l1)
The output will be:
[1, 2, 3, 4, 5, 6]
[1, 2, 3]
Using the += has a different result with the fully expanded operation. What is causing this?

Thats because in the first implementation you are editing the list n itself (and therefore the changes still apply when leaving the function), while on the other implementation you are creating a new temporary list with the same name, so when you leave the function the new list disappears and the variable n is linked to the original list.
the += operator works similarly to x=x+y for immutable objects (since they always create new objects), but for mutable objects such as lists they work differently. x=x+y creats a new object x while x+=y edits the current object.

It may seem counter-intuitive, but they are not always the same. In fact,
a = a + b means a = a.__add__(b), creating a new object
a += b means a = a.__iadd__(b), mutating the object
__iadd__, if absent, defaults to the __add__, but it also can (and it does, in the case of lists) mutate the original object in-place.

This works on how python treats objects and passes variables into functions.
Basically - in first example (with += )
You are passing n and v into function by "pass-by-assignment"
So n gets modified and it will be also modified out of function scope.
In second example - n is reassigned inside of the function to a new list. Which is not seen outside of the function.

In your 1st code. You changes list n itself see the below image..!
In your 2nd code. you just created a temporary list which is cleared when function call ends.. see the below images..!
In the next step when function ends the temporary list clear!!

Python: tuple with mutable items [duplicate]

This question already has answers here:
a mutable type inside an immutable container
(3 answers)
Closed 6 years ago.
So I have this code:
tup = ([1,2,3],[7,8,9])
tup[0] += (4,5,6)
which generates this error:
TypeError: 'tuple' object does not support item assignment
While this code:
tup = ([1,2,3],[7,8,9])
try:
tup[0] += (4,5,6)
except TypeError:
print tup
prints this:
([1, 2, 3, 4, 5, 6], [7, 8, 9])
Is this behavior expected?
Note
I realize this is not a very common use case. However, while the error is expected, I did not expect the list change.

Yes it's expected.
A tuple cannot be changed. A tuple, like a list, is a structure that points to other objects. It doesn't care about what those objects are. They could be strings, numbers, tuples, lists, or other objects.
So doing anything to one of the objects contained in the tuple, including appending to that object if it's a list, isn't relevant to the semantics of the tuple.
(Imagine if you wrote a class that had methods on it that cause its internal state to change. You wouldn't expect it to be impossible to call those methods on an object based on where it's stored).
Or another example:
>>> l1 = [1, 2, 3]
>>> l2 = [4, 5, 6]
>>> t = (l1, l2)
>>> l3 = [l1, l2]
>>> l3[1].append(7)
Two mutable lists referenced by a list and by a tuple. Should I be able to do the last line (answer: yes). If you think the answer's no, why not? Should t change the semantics of l3 (answer: no).
If you want an immutable object of sequential structures, it should be tuples all the way down.
Why does it error?
This example uses the infix operator:
Many operations have an “in-place” version. The following functions
provide a more primitive access to in-place operators than the usual
syntax does; for example, the statement x += y is equivalent to x =
operator.iadd(x, y). Another way to put it is to say that z =
operator.iadd(x, y) is equivalent to the compound statement z = x; z
+= y.
https://docs.python.org/2/library/operator.html
So this:
l = [1, 2, 3]
tup = (l,)
tup[0] += (4,5,6)
is equivalent to this:
l = [1, 2, 3]
tup = (l,)
x = tup[0]
x = x.__iadd__([4, 5, 6]) # like extend, but returns x instead of None
tup[0] = x
The __iadd__ line succeeds, and modifies the first list. So the list has been changed. The __iadd__ call returns the mutated list.
The second line tries to assign the list back to the tuple, and this fails.
So, at the end of the program, the list has been extended but the second part of the += operation failed. For the specifics, see this question.

Well I guess tup[0] += (4, 5, 6) is translated to:
tup[0] = tup[0].__iadd__((4,5,6))
tup[0].__iadd__((4,5,6)) is executed normally changing the list in the first element. But the assignment fails since tuples are immutables.

Tuples cannot be changed directly, correct. Yet, you may change a tuple's element by reference. Like:
>>> tup = ([1,2,3],[7,8,9])
>>> l = tup[0]
>>> l += (4,5,6)
>>> tup
([1, 2, 3, 4, 5, 6], [7, 8, 9])

The Python developers wrote an official explanation about why it happens here: https://docs.python.org/2/faq/programming.html#why-does-a-tuple-i-item-raise-an-exception-when-the-addition-works
The short version is that += actually does two things, one right after the other:
Run the thing on the right.
assign the result to the variable on the left
In this case, step 1 works because you’re allowed to add stuff to lists (they’re mutable), but step 2 fails because you can’t put stuff into tuples after creating them (tuples are immutable).
In a real program, I would suggest you don't do a try-except clause, because tup[0].extend([4,5,6]) does the exact same thing.

Python Variable assignment in a for loop

I understand that in Python regular c++ style variable assignment is replaced by references to stuff ie
a=[1,2,3]
b=a
a.append(4)
print(b) #gives [1,2,3,4]
print(a) #gives [1,2,3,4]
but I'm still confused why an analogous situation with basic types eg. integers works differently?
a=1
b=a
a+=1
print(b) # gives 1
print(a) # gives 2
But wait, it gets even more confusing when we consider loops!
li=[1,2,3]
for x in li:
x+=1
print(li) #gives [1,2,3]
Which is what I expected, but what happens if we do:
a,b,c=1,2,3
li=[a,b,c]
for x in li:
x+=1
print(li) #gives [1,2,3]
Maybe my question should be how to loop over a list of integers and change them without map() as i need a if statement in there. The only thing I can come up short of using
for x in range(len(li)):
Do stuff to li[x]
is packaging the integers in one element list. But there must be a better way.

Well, you need to think of mutable and immutable type.
For a list, it's mutable.
For a integer, it's immutable, which means you will refer to a new object if you change it. When a+=1 is executed, a will be assigned a new object, but b is still refer to the same one.

a=[1,2,3]
b=a
a.append(4)
print(b) #[1,2,3,4]
print(a) #[1,2,3,4]
Here you are modifying the list. The list content changes, but the list identity remains.
a=1
b=a
a+=1
This, however, is a reassignment. You assign a different object to a.
Note that if you did a += [4] in the 1st example, you would have seen the same result. This comes from the fact that a += something is the same as a = a.__iadd__(something), with a fallback to a = a.__add__(something) if __iadd__() doesn't exist.
The difference is that __iadd__() tries to do its job "inplace", by modifying the object it works on and returning it. So a refers to the same as before. This only works with mutable objects such as lists.
On immutable objects such as ints __add__() is called. It returns a different object, which leads to a pointing to another object than before. There is no other choice, as ints are immutable.
a,b,c=1,2,3
li=[a,b,c]
for x in li:
x+=1
print(li) #[1,2,3]
Here x += 1 means the same as x = x + 1. It changes where x refers to, but not the list contents.
Maybe my question should be how to loop over a list of integers and change them without >map() as i need a if statement in there.
for i, x in enumerate(li):
li[i] = x + 1
assigns to every list position the old value + 1.

The important thing here are the variable names. They really are just keys to a dictionary. They are resolved at runtime, depending on the current scope.
Let's have a look what names you access in your code. The locals function helps us: It shows the names in the local scope (and their value). Here's your code, with some debugging output:
a = [1, 2, 3] # a is bound
print(locals())
for x in a: # a is read, and for each iteration x is bound
x = x + 3 # x is read, the value increased and then bound to x again
print(locals())
print(locals())
print(x)
(Note I expanded x += 3 to x = x + 3 to increase visibility for the name accesses - read and write.)
First, you bind the list [1, 2, 3]to the name a. Then, you iterate over the list. During each iteration, the value is bound to the name x in the current scope. Your assignment then assigns another value to x.
Here's the output
{'a': [1, 2, 3]}
{'a': [1, 2, 3], 'x': 4}
{'a': [1, 2, 3], 'x': 5}
{'a': [1, 2, 3], 'x': 6}
{'a': [1, 2, 3], 'x': 6}
6
At no point you're accessing a, the list, and thus will never modify it.
To fix your problem, I'd use the enumerate function to get the index along with the value and then access the list using the name a to change it.
for idx, x in enumerate(a):
a[idx] = x + 3
print(a)
Output:
[4, 5, 6]
Note you might want to wrap those examples in a function, to avoid the cluttered global namespace.
For more about scopes, read the chapter in the Python tutorial. To further investigate that, use the globals function to see the names of the global namespace. (Not to be confused with the global keyword, note the missing 's'.)
Have fun!

For a C++-head it easiest tho think that every Python object is a pointer. When you write a = [1, 2, 3] you essentially write List * a = new List(1, 2, 3). When you write a = b, you essentially write List * b = a.
But when you take out actual items from the lists, these items happen to be numbers. Numbers are immutable; holding a pointer to an immutable object is about as good as holding this object by value.
So your for x in a: x += 1 is essentially
for (int x, it = a.iterator(); it->hasMore(); x=it.next()) {
x+=1; // the generated sum is silently discarded
}
which obviously has no effect.
If list elements were mutable objects you could mutate them exactly the way you wrote. See:
a = [[1], [2], [3]] # list of lists
for x in a: # x iterates over each sub-list
x.append(10)
print a # prints [[1, 10], [2, 10], [3, 10]]
But unless you have a compelling reason (e.g. a list of millions of objects under heavy memory load) you are better off making a copy of the list, applying a transformation and optionally a filter. This is easily done with a list comprehension:
a = [1, 2, 3, 0]
b = [n + 1 for n in a] # [2, 3, 4, 1]
c = [n * 10 for n in a if n < 3] # [10, 20, 0]
Either that, or you can write an explicit loop that creates another list:
source = [1, 2, 3]
target = []
for n in source:
n1 = <many lines of code involving n>
target.append(n1)

Your question has multiple parts, so it's going to be hard for one answer to cover all of them. glglgl has done a great job on most of it, but your final question is still unexplained:
Maybe my question should be how to loop over a list of integers and change them without map() as i need a if statement in there
"I need an if statement in there" doesn't mean you can't use map.
First, if you want the if to select which values you want to keep, map has a good friend named filter that does exactly that. For example, to keep only the odd numbers, but add one to each of them, you could do this:
>>> a = [1, 2, 3, 4, 5]
>>> b = []
>>> for x in a:
... if x%2:
... b.append(x+1)
Or just this:
>>> b = map(lambda x: x+1, filter(lambda x: x%2, a))
If, on the other hand, you want the if to control the expression itself—e.g., to add 1 to the odd numbers but leave the even ones alone, you can use an if expression the same way you'd use an if statement:
>>> for x in a:
... if x%2:
... b.append(x+1)
... else:
... b.append(x)
>>> b = map(lambda x: x+1 if x%2 else x, a)
Second, comprehensions are basically equivalent to map and filter, but with expressions instead of functions. If your expression would just be "call this function", then use map or filter. If your function would just be a lambda to "evaluate this expression", then use a comprehension. The above two examples get more readable this way:
>>> b = [x+1 for x in a if x%2]
>>> b = [x+1 if x%2 else x for x in a]

You can do something like this: li = [x+1 for x in li]

function that given a list returns a list of list decreasing

I'd make a function in python, that given a list returns a list of list, in which every element is the list given decreased by one.
Input: list_decreaser([0,3,4,5,6,7,8)
Output: [[0,3,4,5,6,7],[0,3,4,5,6],[0,3,4,5],[0,3,4],[0,3],[0]]
My attempt:
def list_decreaser(list):
listresult = []
for x in range(len(list)-1):
list.remove(list[x])
listresult.append(list)
return listresult

The code appends the same list multiple times. It should append copy of the list.
And use del list[..] instead of list.remove(list[..]) to delete an item at specific index.
def list_decreaser(xs):
listresult = []
for i in range(len(xs)-1, 0, -1): # <--- interate backward
del xs[i]
listresult.append(xs[:]) # <----
return listresult
print(list_decreaser([0,3,4,5,6,7,8]))
Or using list comprehension:
>>> xs = [0,3,4,5,6,7,8]
>>> [xs[:i] for i in range(len(xs)-1, 0, -1)]
[[0, 3, 4, 5, 6, 7], [0, 3, 4, 5, 6], [0, 3, 4, 5], [0, 3, 4], [0, 3], [0]]
BTW, don't use list as a variable name. It shadows builtin list function.

The problem is that you're appending the same list over and over again. You keep mutating the list in-place, but you're never creating a new list. So you end up with a list of N references to the same empty list.
This is the same problem discussed in two FAQ questions. I think How do I create a multidimensional list explains it best.
Anyway, what you need to do is append a new list each time through the loop. There are two ways to do that.
First, you can append a copy of the current list, instead of the list itself:
def list_decreaser(list):
listresult = []
for x in range(len(list)-1):
list.remove(list[x])
listresult.append(list[:]) # this is the only change
return listresult
This solves your problem, but it leaves a few new problems:
First, list.remove(list[x]) is a very bad idea. If you give it, say, [0, 1, 2, 0], what happens when you try to remove that second 0? You're calling list.remove(0), and there's no way the list can know you wanted the second 0 rather than the first! The right thing to do is call del list[x] or list.pop(x).
But once you fix that, you're removing the elements from the wrong side. x is 0, then 1, then 2, and so on. You remove element 0, then element 1 (which is the original element 2), then element 2 (which is the original element 4), and eventually get an IndexError. Even if you fixed the "skipping an index" issue (which is also explained in the FAQ somewhere), you'd still be removing the first elements rather than the last ones. You can fix that by turning the range around. However, there's an even easier way: Just remove the last element each time, instead of trying to figure out which x is the right thing, which you can do by specifying -1, or just calling pop with no argument. And then you can use a much simpler loop, too:
def list_decreaser(list):
listresult = []
while list:
list.pop()
listresult.append(list[:])
return listresult
Of course this appends the last, empty list, which you apparently didn't want. You can fix that by doing while len(list) >= 1, or putting an if list: listresult.append(list[:]), or in various other ways.
Alternatively, you can make new truncated lists instead of truncating and copying the same list over and over:
def list_decreaser(list):
listresult = []
while len(list):
list = list[:-1]
listresult.append(list)
return listresult
Note that in this second version, rather than changing the value stored in list, we're creating a new list and storing that new list in list.

use this
def list_decreaser(list1):
listresult = []
for i in list1:
list1 = list[:-1]
listresult.append(list1)
return listresult

Python index usage

Here is some code:
# Question 9: Deep Reverse
# Define a procedure, deep_reverse, that takes as input a list,
# and returns a new list that is the deep reverse of the input list.
# This means it reverses all the elements in the list, and if any
# of those elements are lists themselves, reverses all the elements
# in the inner list, all the way down.
# Note: The procedure must not change the input list.
# The procedure is_list below is from Homework 6. It returns True if
# p is a list and False if it is not.
def is_list(p):
return isinstance(p, list)
#For example,
def deep_reverse(n):
n.reverse()
for entry in n:
if is_list(entry):
entry.reverse()
deep_reverseA(entry)
return n
def deep_reverseA(n):
for entry in n:
if is_list(entry):
entry.reverse()
deep_reverseA(entry)
return n
p = [1, [2, 3, [4, [5, 6]]]]
print deep_reverse(p)
#>>> [[[[6, 5], 4], 3, 2], 1]
print p
#>>> [1, [2, 3, [4, [5, 6]]]]
q = [1, [2,3], 4, [5,6]]
print deep_reverse(q)
#>>> [ [6,5], 4, [3, 2], 1]
print q
#>>> [1, [2,3], 4, [5,6]]
My problem is that once I run the code the values of p and q change. How can I make them not change. I know that in python indexes are connected so if indexA = indexB and you change indexA then indexB will change. That is the problem I am having with fixing this problem.

I'll just tell you the answer right here, right now, with an explanation.
In python, variables are merely pointers to stored objects. So as you said in your post, if you declare foo = bar then foo not only is equal to bar, but foo is bar. This won't change unless you explicitly say so (for example you set bar = 2). So you need a way to make a copy of the original list.
There's this thing in python called list slicing, and I'm sure you heard of it. Basically you can get a portion of the list from indexA to indexB with my_list[indexA:indexB].
But you can also leave these spaces blank. indexA if not specified defaults to 0, and indexB defaults to -1 (the last element of the list).
So my_list[2:] returns all the elements from my_list[2] to my_list[-1]. Likewise, my_list[:3] returns my_list[0] to my_list[3].
Therefore, calling my_list[:] returns an exact copy of my_list, but not the actual list itself. This is what you need to do.
So applying it to your code:
def deep_reverse(n):
ncopy = n[:] #this is the part you need
#rest of function, replace `n` with `ncopy`
return ncopy
Also, do not apply this to deep_reverseA because in that function you are changing the original list in the copied list. You aren't changing the list you input into deep_reverse. If you did apply this to deep_reverseA, the lists wouldn't actually change (you would be returning a reverse of the copy but not the original)