I was told that += can have different effects than the standard notation of i = i +. Is there a case in which i += 1 would be different from i = i + 1?
This depends entirely on the object i.
+= calls the __iadd__ method (if it exists -- falling back on __add__ if it doesn't exist) whereas + calls the __add__ method1 or the __radd__ method in a few cases2.
From an API perspective, __iadd__ is supposed to be used for modifying mutable objects in place (returning the object which was mutated) whereas __add__ should return a new instance of something. For immutable objects, both methods return a new instance, but __iadd__ will put the new instance in the current namespace with the same name that the old instance had. This is why
i = 1
i += 1
seems to increment i. In reality, you get a new integer and assign it "on top of" i -- losing one reference to the old integer. In this case, i += 1 is exactly the same as i = i + 1. But, with most mutable objects, it's a different story:
As a concrete example:
a = [1, 2, 3]
b = a
b += [1, 2, 3]
print(a) # [1, 2, 3, 1, 2, 3]
print(b) # [1, 2, 3, 1, 2, 3]
compared to:
a = [1, 2, 3]
b = a
b = b + [1, 2, 3]
print(a) # [1, 2, 3]
print(b) # [1, 2, 3, 1, 2, 3]
notice how in the first example, since b and a reference the same object, when I use += on b, it actually changes b (and a sees that change too -- After all, it's referencing the same list). In the second case however, when I do b = b + [1, 2, 3], this takes the list that b is referencing and concatenates it with a new list [1, 2, 3]. It then stores the concatenated list in the current namespace as b -- With no regard for what b was the line before.
1In the expression x + y, if x.__add__ isn't implemented or if x.__add__(y) returns NotImplemented and x and y have different types, then x + y tries to call y.__radd__(x). So, in the case where you have
foo_instance += bar_instance
if Foo doesn't implement __add__ or __iadd__ then the result here is the same as
foo_instance = bar_instance.__radd__(bar_instance, foo_instance)
2In the expression foo_instance + bar_instance, bar_instance.__radd__ will be tried before foo_instance.__add__ if the type of bar_instance is a subclass of the type of foo_instance (e.g. issubclass(Bar, Foo)). The rationale for this is that Bar is in some sense a "higher-level" object than Foo so Bar should get the option of overriding Foo's behavior.
Under the covers, i += 1 does something like this:
try:
i = i.__iadd__(1)
except AttributeError:
i = i.__add__(1)
While i = i + 1 does something like this:
i = i.__add__(1)
This is a slight oversimplification, but you get the idea: Python gives types a way to handle += specially, by creating an __iadd__ method as well as an __add__.
The intention is that mutable types, like list, will mutate themselves in __iadd__ (and then return self, unless you're doing something very tricky), while immutable types, like int, will just not implement it.
For example:
>>> l1 = []
>>> l2 = l1
>>> l1 += [3]
>>> l2
[3]
Because l2 is the same object as l1, and you mutated l1, you also mutated l2.
But:
>>> l1 = []
>>> l2 = l1
>>> l1 = l1 + [3]
>>> l2
[]
Here, you didn't mutate l1; instead, you created a new list, l1 + [3], and rebound the name l1 to point at it, leaving l2 pointing at the original list.
(In the += version, you were also rebinding l1, it's just that in that case you were rebinding it to the same list it was already bound to, so you can usually ignore that part.)
Here is an example that directly compares i += x with i = i + x:
def foo(x):
x = x + [42]
def bar(x):
x += [42]
c = [27]
foo(c); # c is not changed
bar(c); # c is changed to [27, 42]
This question already has answers here:
How are Python in-place operator functions different than the standard operator functions?
(2 answers)
Closed 5 years ago.
Please look into the code below
def double(arg):
print("Before: ", arg)
arg = arg * 2
print("After: ", arg)
I was studying Head first Python, and I came to this section where they were discussing about pass by value and pass by reference.
If we invoke above function with a list as argument such as:
num = [1,2,3]
double(num)
print(num)
The output is :-
Before: [1, 2, 3]
After: [1, 2, 3, 1, 2, 3]
[1, 2, 3]
Which seems fine, considering the fact that arg in function double is a new object reference. So the value of num did not change.
But if I use compound operators instead of assignment operators, things work differently as shown:
def double(arg):
print("Before: ", arg)
arg *= 2
print("After: ", arg)
num = [1,2,3]
double(num)
print(num)
The output that I get for this is:
Before: [1, 2, 3]
After: [1, 2, 3, 1, 2, 3]
[1, 2, 3, 1, 2, 3]
Why does this happen? I used to think a*=2 and a = a*2 are same. But what's going on in here?
Thanks
This is a difference between mutable and immutable objects. A mutable object can implement obj *= something by actually modifying the object in place; an immutable object can only return a new object with the updated value (in which case the result is identical to obj = obj * something). The compound assignment statements can handle either case, it's entirely up to the object's implementation.
a *= 2 modifies the structure itself (a) whereas a = a*2 reassigns a as a new variable.
(see this question)
I was told that += can have different effects than the standard notation of i = i +. Is there a case in which i += 1 would be different from i = i + 1?
This depends entirely on the object i.
+= calls the __iadd__ method (if it exists -- falling back on __add__ if it doesn't exist) whereas + calls the __add__ method1 or the __radd__ method in a few cases2.
From an API perspective, __iadd__ is supposed to be used for modifying mutable objects in place (returning the object which was mutated) whereas __add__ should return a new instance of something. For immutable objects, both methods return a new instance, but __iadd__ will put the new instance in the current namespace with the same name that the old instance had. This is why
i = 1
i += 1
seems to increment i. In reality, you get a new integer and assign it "on top of" i -- losing one reference to the old integer. In this case, i += 1 is exactly the same as i = i + 1. But, with most mutable objects, it's a different story:
As a concrete example:
a = [1, 2, 3]
b = a
b += [1, 2, 3]
print(a) # [1, 2, 3, 1, 2, 3]
print(b) # [1, 2, 3, 1, 2, 3]
compared to:
a = [1, 2, 3]
b = a
b = b + [1, 2, 3]
print(a) # [1, 2, 3]
print(b) # [1, 2, 3, 1, 2, 3]
notice how in the first example, since b and a reference the same object, when I use += on b, it actually changes b (and a sees that change too -- After all, it's referencing the same list). In the second case however, when I do b = b + [1, 2, 3], this takes the list that b is referencing and concatenates it with a new list [1, 2, 3]. It then stores the concatenated list in the current namespace as b -- With no regard for what b was the line before.
1In the expression x + y, if x.__add__ isn't implemented or if x.__add__(y) returns NotImplemented and x and y have different types, then x + y tries to call y.__radd__(x). So, in the case where you have
foo_instance += bar_instance
if Foo doesn't implement __add__ or __iadd__ then the result here is the same as
foo_instance = bar_instance.__radd__(bar_instance, foo_instance)
2In the expression foo_instance + bar_instance, bar_instance.__radd__ will be tried before foo_instance.__add__ if the type of bar_instance is a subclass of the type of foo_instance (e.g. issubclass(Bar, Foo)). The rationale for this is that Bar is in some sense a "higher-level" object than Foo so Bar should get the option of overriding Foo's behavior.
Under the covers, i += 1 does something like this:
try:
i = i.__iadd__(1)
except AttributeError:
i = i.__add__(1)
While i = i + 1 does something like this:
i = i.__add__(1)
This is a slight oversimplification, but you get the idea: Python gives types a way to handle += specially, by creating an __iadd__ method as well as an __add__.
The intention is that mutable types, like list, will mutate themselves in __iadd__ (and then return self, unless you're doing something very tricky), while immutable types, like int, will just not implement it.
For example:
>>> l1 = []
>>> l2 = l1
>>> l1 += [3]
>>> l2
[3]
Because l2 is the same object as l1, and you mutated l1, you also mutated l2.
But:
>>> l1 = []
>>> l2 = l1
>>> l1 = l1 + [3]
>>> l2
[]
Here, you didn't mutate l1; instead, you created a new list, l1 + [3], and rebound the name l1 to point at it, leaving l2 pointing at the original list.
(In the += version, you were also rebinding l1, it's just that in that case you were rebinding it to the same list it was already bound to, so you can usually ignore that part.)
Here is an example that directly compares i += x with i = i + x:
def foo(x):
x = x + [42]
def bar(x):
x += [42]
c = [27]
foo(c); # c is not changed
bar(c); # c is changed to [27, 42]
I tried to use multiple assignment as show below to initialize variables, but I got confused by the behavior, I expect to reassign the values list separately, I mean b[0] and c[0] equal 0 as before.
a=b=c=[0,3,5]
a[0]=1
print(a)
print(b)
print(c)
Result is:
[1, 3, 5]
[1, 3, 5]
[1, 3, 5]
Is that correct? what should I use for multiple assignment?
what is different from this?
d=e=f=3
e=4
print('f:',f)
print('e:',e)
result:
('f:', 3)
('e:', 4)
If you're coming to Python from a language in the C/Java/etc. family, it may help you to stop thinking about a as a "variable", and start thinking of it as a "name".
a, b, and c aren't different variables with equal values; they're different names for the same identical value. Variables have types, identities, addresses, and all kinds of stuff like that.
Names don't have any of that. Values do, of course, and you can have lots of names for the same value.
If you give Notorious B.I.G. a hot dog,* Biggie Smalls and Chris Wallace have a hot dog. If you change the first element of a to 1, the first elements of b and c are 1.
If you want to know if two names are naming the same object, use the is operator:
>>> a=b=c=[0,3,5]
>>> a is b
True
You then ask:
what is different from this?
d=e=f=3
e=4
print('f:',f)
print('e:',e)
Here, you're rebinding the name e to the value 4. That doesn't affect the names d and f in any way.
In your previous version, you were assigning to a[0], not to a. So, from the point of view of a[0], you're rebinding a[0], but from the point of view of a, you're changing it in-place.
You can use the id function, which gives you some unique number representing the identity of an object, to see exactly which object is which even when is can't help:
>>> a=b=c=[0,3,5]
>>> id(a)
4473392520
>>> id(b)
4473392520
>>> id(a[0])
4297261120
>>> id(b[0])
4297261120
>>> a[0] = 1
>>> id(a)
4473392520
>>> id(b)
4473392520
>>> id(a[0])
4297261216
>>> id(b[0])
4297261216
Notice that a[0] has changed from 4297261120 to 4297261216—it's now a name for a different value. And b[0] is also now a name for that same new value. That's because a and b are still naming the same object.
Under the covers, a[0]=1 is actually calling a method on the list object. (It's equivalent to a.__setitem__(0, 1).) So, it's not really rebinding anything at all. It's like calling my_object.set_something(1). Sure, likely the object is rebinding an instance attribute in order to implement this method, but that's not what's important; what's important is that you're not assigning anything, you're just mutating the object. And it's the same with a[0]=1.
user570826 asked:
What if we have, a = b = c = 10
That's exactly the same situation as a = b = c = [1, 2, 3]: you have three names for the same value.
But in this case, the value is an int, and ints are immutable. In either case, you can rebind a to a different value (e.g., a = "Now I'm a string!"), but the won't affect the original value, which b and c will still be names for. The difference is that with a list, you can change the value [1, 2, 3] into [1, 2, 3, 4] by doing, e.g., a.append(4); since that's actually changing the value that b and c are names for, b will now b [1, 2, 3, 4]. There's no way to change the value 10 into anything else. 10 is 10 forever, just like Claudia the vampire is 5 forever (at least until she's replaced by Kirsten Dunst).
* Warning: Do not give Notorious B.I.G. a hot dog. Gangsta rap zombies should never be fed after midnight.
Cough cough
>>> a,b,c = (1,2,3)
>>> a
1
>>> b
2
>>> c
3
>>> a,b,c = ({'test':'a'},{'test':'b'},{'test':'c'})
>>> a
{'test': 'a'}
>>> b
{'test': 'b'}
>>> c
{'test': 'c'}
>>>
In python, everything is an object, also "simple" variables types (int, float, etc..).
When you changes a variable value, you actually changes it's pointer, and if you compares between two variables it's compares their pointers.
(To be clear, pointer is the address in physical computer memory where a variable is stored).
As a result, when you changes an inner variable value, you changes it's value in the memory and it's affects all the variables that point to this address.
For your example, when you do:
a = b = 5
This means that a and b points to the same address in memory that contains the value 5, but when you do:
a = 6
It's not affect b because a is now points to another memory location that contains 6 and b still points to the memory address that contains 5.
But, when you do:
a = b = [1,2,3]
a and b, again, points to the same location but the difference is that if you change the one of the list values:
a[0] = 2
It's changes the value of the memory that a is points on, but a is still points to the same address as b, and as a result, b changes as well.
Yes, that's the expected behavior. a, b and c are all set as labels for the same list. If you want three different lists, you need to assign them individually. You can either repeat the explicit list, or use one of the numerous ways to copy a list:
b = a[:] # this does a shallow copy, which is good enough for this case
import copy
c = copy.deepcopy(a) # this does a deep copy, which matters if the list contains mutable objects
Assignment statements in Python do not copy objects - they bind the name to an object, and an object can have as many labels as you set. In your first edit, changing a[0], you're updating one element of the single list that a, b, and c all refer to. In your second, changing e, you're switching e to be a label for a different object (4 instead of 3).
You can use id(name) to check if two names represent the same object:
>>> a = b = c = [0, 3, 5]
>>> print(id(a), id(b), id(c))
46268488 46268488 46268488
Lists are mutable; it means you can change the value in place without creating a new object. However, it depends on how you change the value:
>>> a[0] = 1
>>> print(id(a), id(b), id(c))
46268488 46268488 46268488
>>> print(a, b, c)
[1, 3, 5] [1, 3, 5] [1, 3, 5]
If you assign a new list to a, then its id will change, so it won't affect b and c's values:
>>> a = [1, 8, 5]
>>> print(id(a), id(b), id(c))
139423880 46268488 46268488
>>> print(a, b, c)
[1, 8, 5] [1, 3, 5] [1, 3, 5]
Integers are immutable, so you cannot change the value without creating a new object:
>>> x = y = z = 1
>>> print(id(x), id(y), id(z))
507081216 507081216 507081216
>>> x = 2
>>> print(id(x), id(y), id(z))
507081248 507081216 507081216
>>> print(x, y, z)
2 1 1
in your first example a = b = c = [1, 2, 3] you are really saying:
'a' is the same as 'b', is the same as 'c' and they are all [1, 2, 3]
If you want to set 'a' equal to 1, 'b' equal to '2' and 'c' equal to 3, try this:
a, b, c = [1, 2, 3]
print(a)
--> 1
print(b)
--> 2
print(c)
--> 3
Hope this helps!
What you need is this:
a, b, c = [0,3,5] # Unpack the list, now a, b, and c are ints
a = 1 # `a` did equal 0, not [0,3,5]
print(a)
print(b)
print(c)
Simply put, in the first case, you are assigning multiple names to a list. Only one copy of list is created in memory and all names refer to that location. So changing the list using any of the names will actually modify the list in memory.
In the second case, multiple copies of same value are created in memory. So each copy is independent of one another.
The code that does what I need could be this:
# test
aux=[[0 for n in range(3)] for i in range(4)]
print('aux:',aux)
# initialization
a,b,c,d=[[0 for n in range(3)] for i in range(4)]
# changing values
a[0]=1
d[2]=5
print('a:',a)
print('b:',b)
print('c:',c)
print('d:',d)
Result:
('aux:', [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]])
('a:', [1, 0, 0])
('b:', [0, 0, 0])
('c:', [0, 0, 0])
('d:', [0, 0, 5])
To assign multiple variables same value I prefer list
a, b, c = [10]*3#multiplying 3 because we have 3 variables
print(a, type(a), b, type(b), c, type(c))
output:
10 <class 'int'> 10 <class 'int'> 10 <class 'int'>
Initialize multiple objects:
import datetime
time1, time2, time3 = [datetime.datetime.now()]*3
print(time1)
print(time2)
print(time3)
output:
2022-02-25 11:52:59.064487
2022-02-25 11:52:59.064487
2022-02-25 11:52:59.064487
E.g: basically a = b = 10 means both a and b are pointing to 10 in the memory, you can test by id(a) and id(b) which comes out exactly equal to a is b as True.
is matches the memory location but not its value, however == matches the value.
let's suppose, you want to update the value of a from 10 to 5, since the memory location was pointing to the same memory location you will experience the value of b will also be pointing to 5 because of the initial declaration.
The conclusion is to use this only if you know the consequences otherwise simply use , separated assignment like a, b = 10, 10 and won't face the above-explained consequences on updating any of the values because of different memory locations.
The behavior is correct. However, all the variables will share the same reference. Please note the behavior below:
>>> a = b = c = [0,1,2]
>>> a
[0, 1, 2]
>>> b
[0, 1, 2]
>>> c
[0, 1, 2]
>>> a[0]=1000
>>> a
[1000, 1, 2]
>>> b
[1000, 1, 2]
>>> c
[1000, 1, 2]
So, yes, it is different in the sense that if you assign a, b and c differently on a separate line, changing one will not change the others.
Here are two codes for you to choose one:
a = b = c = [0, 3, 5]
a = [1, 3, 5]
print(a)
print(b)
print(c)
or
a = b = c = [0, 3, 5]
a = [1] + a[1:]
print(a)
print(b)
print(c)