let's say I have Obj_A (RigidBody2D) & Obj_B (RigidBody2D)
I want to check if these 2 CAN collide
so far my idea is checking the mutual layers in both their collision_mask & collision_layer
the problem I'm facing is that Obj_A.collision_mask & (collision_layer) does not return an array of the layers and instead returns binary
so is there any inbuilt function to check for it?
or is there some different approach altogether?
Use bitwise operations.
Imagine the operation you want, but for a single bit. So a bool. And we want to get true when they are both true. And false otherwise. We write a truth table for that if we want to:
"collision_mask"
"collision_layer"
desired result
false
false
false
false
true
false
true
false
false
true
true
true
I'm writing "collision_mask" and "collision_layer" in quotes here as reminder that this is an hypothetical in which they are bool, accounting for only one possible layer (bit).
We observe from the table that, of course, the operation we want is conjunction (and).
Now, the bitwise conjunction operator is &. It works with ints and will perform the operation… bitwise. For example:
var a := 0b1100 # Binary literal for 12
var b := 0b1010 # Binary literal for 10
var c := 0b1000 # Binary literal for 8
print (c == a & b) # true
As you can see, it does not give you a true or false value. Instead what we get is an int where each bit is the result of doing the operation on the bits that are on the same position on the inputs.
In consequence, when we use &, if the inputs have bits that are true 1 on the same position, the bit on that same position but on the result will also be true 1.
Say that in negative terms: if none of bits of the inputs are true 1 on the same position, then all the bits on the result will be false 0.
And we can check that!
Ergo:
if (Obj_A.collision_mask & Obj_B.collision_layer) == 0:
# no possible collision
pass
Or, you know, negate it:
if (Obj_A.collision_mask & Obj_B.collision_layer) != 0:
# possible collision
pass
And, yes, check both ways:
if (
(Obj_A.collision_mask & Obj_B.collision_layer) != 0
and (Obj_A.collision_layer & Obj_B.collision_mask) != 0
):
# possible collision
pass
For reference these are the bitwise operators in GDScript:
operation
bool
alternative
bitwise
negation
not
!
~
conjunction
and
&&
&
disjunction
or
||
|
exclusive disjunction
^
Note that there is a bitwise exclusive disjunction operator (^), but not a corresponding bool operator (there is no xor operator).
I also want to mention that GDScript has bit shifting operators: << and >>.
If you rather not use bitwise operators, you can instead use get_collision_layer_bit and get_collision_mask_bit. With knowledge that there are 32 possible layers, you can do this:
var can_collide := false
for layer in 32:
if Obj_A.get_collision_mask_bit(layer) and Obj_B.get_collision_layer_bit(layer):
can_collide = true
if can_collide:
# possible collision
pass
That is the same logic that we are executing with the bitwise operations. Except much less succinct and with worse performance.
Also for reference, this is get_collision_mask_bit is implemented:
bool CollisionObject::get_collision_mask_bit(int p_bit) const {
ERR_FAIL_INDEX_V_MSG(p_bit, 32, false, "Collision mask bit must be between 0 and 31 inclusive.");
return get_collision_mask() & (1 << p_bit);
}
Which you could write in GDScript like this:
func get_collision_mask_bit(bit:int) -> bool:
if bit >= 32:
push_error("Collision mask bit must be between 0 and 31 inclusive.")
return
return 0 != collision_mask & (1 << bit)
I bring this up to highlight that these are built ontop of the bitwise operators, and not the other way around.
It is also an example of use of a bit shift operator.
I think I've figured it out, turns out it was simpler than I thought :P
func will_collide(obj1,obj2):
return (obj1.collision_layer & obj2.collision_mask) || (obj1.collision_layer & obj2.collision_mask)
Related
Define a new function named too_many_kittens that takes three arguments, in order:
kittens, the number of kittens
litterboxes, the (integer) number of available litterboxes
catfood, a boolean representing whether or not any catfood exists
In the function return the value of the following comparison statement:
not (litterboxes >= kittens and catfood)
This statement ensures we have at least one litterbox for each kitten and that we have some catfood. It then uses inversion via not to answer whether or not we have too many kittens.
Note
Note the spacing of the not operator. There should always be spacing around all logical operators like and, not or or. Without it, not would look like a function, eg not().
Note
A fun fact of the polymorphic properties of python is the fact that truthiness would allow catfood to either be a boolean (eg, True) or some number like 0 or even None and this would continue to operate in a reasonably sane manner.
Expected Output
>>> too_many_kittens(12, 12, False)
True
>>> too_many_kittens(13, 12, True)
True
>>> too_many_kittens(12, 13, True)
False
def too_many_kittens(kittens, letterboxes, bool(cat food))
not (letterboxes >= kittens and catfood)
This question's answers are a community effort. Edit existing answers to improve this post. It is not currently accepting new answers or interactions.
My Google-fu has failed me.
In Python, are the following two tests for equality equivalent?
n = 5
# Test one.
if n == 5:
print 'Yay!'
# Test two.
if n is 5:
print 'Yay!'
Does this hold true for objects where you would be comparing instances (a list say)?
Okay, so this kind of answers my question:
L = []
L.append(1)
if L == [1]:
print 'Yay!'
# Holds true, but...
if L is [1]:
print 'Yay!'
# Doesn't.
So == tests value where is tests to see if they are the same object?
is will return True if two variables point to the same object (in memory), == if the objects referred to by the variables are equal.
>>> a = [1, 2, 3]
>>> b = a
>>> b is a
True
>>> b == a
True
# Make a new copy of list `a` via the slice operator,
# and assign it to variable `b`
>>> b = a[:]
>>> b is a
False
>>> b == a
True
In your case, the second test only works because Python caches small integer objects, which is an implementation detail. For larger integers, this does not work:
>>> 1000 is 10**3
False
>>> 1000 == 10**3
True
The same holds true for string literals:
>>> "a" is "a"
True
>>> "aa" is "a" * 2
True
>>> x = "a"
>>> "aa" is x * 2
False
>>> "aa" is intern(x*2)
True
Please see this question as well.
There is a simple rule of thumb to tell you when to use == or is.
== is for value equality. Use it when you would like to know if two objects have the same value.
is is for reference equality. Use it when you would like to know if two references refer to the same object.
In general, when you are comparing something to a simple type, you are usually checking for value equality, so you should use ==. For example, the intention of your example is probably to check whether x has a value equal to 2 (==), not whether x is literally referring to the same object as 2.
Something else to note: because of the way the CPython reference implementation works, you'll get unexpected and inconsistent results if you mistakenly use is to compare for reference equality on integers:
>>> a = 500
>>> b = 500
>>> a == b
True
>>> a is b
False
That's pretty much what we expected: a and b have the same value, but are distinct entities. But what about this?
>>> c = 200
>>> d = 200
>>> c == d
True
>>> c is d
True
This is inconsistent with the earlier result. What's going on here? It turns out the reference implementation of Python caches integer objects in the range -5..256 as singleton instances for performance reasons. Here's an example demonstrating this:
>>> for i in range(250, 260): a = i; print "%i: %s" % (i, a is int(str(i)));
...
250: True
251: True
252: True
253: True
254: True
255: True
256: True
257: False
258: False
259: False
This is another obvious reason not to use is: the behavior is left up to implementations when you're erroneously using it for value equality.
Is there a difference between == and is in Python?
Yes, they have a very important difference.
==: check for equality - the semantics are that equivalent objects (that aren't necessarily the same object) will test as equal. As the documentation says:
The operators <, >, ==, >=, <=, and != compare the values of two objects.
is: check for identity - the semantics are that the object (as held in memory) is the object. Again, the documentation says:
The operators is and is not test for object identity: x is y is true
if and only if x and y are the same object. Object identity is
determined using the id() function. x is not y yields the inverse
truth value.
Thus, the check for identity is the same as checking for the equality of the IDs of the objects. That is,
a is b
is the same as:
id(a) == id(b)
where id is the builtin function that returns an integer that "is guaranteed to be unique among simultaneously existing objects" (see help(id)) and where a and b are any arbitrary objects.
Other Usage Directions
You should use these comparisons for their semantics. Use is to check identity and == to check equality.
So in general, we use is to check for identity. This is usually useful when we are checking for an object that should only exist once in memory, referred to as a "singleton" in the documentation.
Use cases for is include:
None
enum values (when using Enums from the enum module)
usually modules
usually class objects resulting from class definitions
usually function objects resulting from function definitions
anything else that should only exist once in memory (all singletons, generally)
a specific object that you want by identity
Usual use cases for == include:
numbers, including integers
strings
lists
sets
dictionaries
custom mutable objects
other builtin immutable objects, in most cases
The general use case, again, for ==, is the object you want may not be the same object, instead it may be an equivalent one
PEP 8 directions
PEP 8, the official Python style guide for the standard library also mentions two use-cases for is:
Comparisons to singletons like None should always be done with is or
is not, never the equality operators.
Also, beware of writing if x when you really mean if x is not None --
e.g. when testing whether a variable or argument that defaults to None
was set to some other value. The other value might have a type (such
as a container) that could be false in a boolean context!
Inferring equality from identity
If is is true, equality can usually be inferred - logically, if an object is itself, then it should test as equivalent to itself.
In most cases this logic is true, but it relies on the implementation of the __eq__ special method. As the docs say,
The default behavior for equality comparison (== and !=) is based on
the identity of the objects. Hence, equality comparison of instances
with the same identity results in equality, and equality comparison of
instances with different identities results in inequality. A
motivation for this default behavior is the desire that all objects
should be reflexive (i.e. x is y implies x == y).
and in the interests of consistency, recommends:
Equality comparison should be reflexive. In other words, identical
objects should compare equal:
x is y implies x == y
We can see that this is the default behavior for custom objects:
>>> class Object(object): pass
>>> obj = Object()
>>> obj2 = Object()
>>> obj == obj, obj is obj
(True, True)
>>> obj == obj2, obj is obj2
(False, False)
The contrapositive is also usually true - if somethings test as not equal, you can usually infer that they are not the same object.
Since tests for equality can be customized, this inference does not always hold true for all types.
An exception
A notable exception is nan - it always tests as not equal to itself:
>>> nan = float('nan')
>>> nan
nan
>>> nan is nan
True
>>> nan == nan # !!!!!
False
Checking for identity can be much a much quicker check than checking for equality (which might require recursively checking members).
But it cannot be substituted for equality where you may find more than one object as equivalent.
Note that comparing equality of lists and tuples will assume that identity of objects are equal (because this is a fast check). This can create contradictions if the logic is inconsistent - as it is for nan:
>>> [nan] == [nan]
True
>>> (nan,) == (nan,)
True
A Cautionary Tale:
The question is attempting to use is to compare integers. You shouldn't assume that an instance of an integer is the same instance as one obtained by another reference. This story explains why.
A commenter had code that relied on the fact that small integers (-5 to 256 inclusive) are singletons in Python, instead of checking for equality.
Wow, this can lead to some insidious bugs. I had some code that checked if a is b, which worked as I wanted because a and b are typically small numbers. The bug only happened today, after six months in production, because a and b were finally large enough to not be cached. – gwg
It worked in development. It may have passed some unittests.
And it worked in production - until the code checked for an integer larger than 256, at which point it failed in production.
This is a production failure that could have been caught in code review or possibly with a style-checker.
Let me emphasize: do not use is to compare integers.
== determines if the values are equal, while is determines if they are the exact same object.
What's the difference between is and ==?
== and is are different comparison! As others already said:
== compares the values of the objects.
is compares the references of the objects.
In Python names refer to objects, for example in this case value1 and value2 refer to an int instance storing the value 1000:
value1 = 1000
value2 = value1
Because value2 refers to the same object is and == will give True:
>>> value1 == value2
True
>>> value1 is value2
True
In the following example the names value1 and value2 refer to different int instances, even if both store the same integer:
>>> value1 = 1000
>>> value2 = 1000
Because the same value (integer) is stored == will be True, that's why it's often called "value comparison". However is will return False because these are different objects:
>>> value1 == value2
True
>>> value1 is value2
False
When to use which?
Generally is is a much faster comparison. That's why CPython caches (or maybe reuses would be the better term) certain objects like small integers, some strings, etc. But this should be treated as implementation detail that could (even if unlikely) change at any point without warning.
You should only use is if you:
want to check if two objects are really the same object (not just the same "value"). One example can be if you use a singleton object as constant.
want to compare a value to a Python constant. The constants in Python are:
None
True1
False1
NotImplemented
Ellipsis
__debug__
classes (for example int is int or int is float)
there could be additional constants in built-in modules or 3rd party modules. For example np.ma.masked from the NumPy module)
In every other case you should use == to check for equality.
Can I customize the behavior?
There is some aspect to == that hasn't been mentioned already in the other answers: It's part of Pythons "Data model". That means its behavior can be customized using the __eq__ method. For example:
class MyClass(object):
def __init__(self, val):
self._value = val
def __eq__(self, other):
print('__eq__ method called')
try:
return self._value == other._value
except AttributeError:
raise TypeError('Cannot compare {0} to objects of type {1}'
.format(type(self), type(other)))
This is just an artificial example to illustrate that the method is really called:
>>> MyClass(10) == MyClass(10)
__eq__ method called
True
Note that by default (if no other implementation of __eq__ can be found in the class or the superclasses) __eq__ uses is:
class AClass(object):
def __init__(self, value):
self._value = value
>>> a = AClass(10)
>>> b = AClass(10)
>>> a == b
False
>>> a == a
So it's actually important to implement __eq__ if you want "more" than just reference-comparison for custom classes!
On the other hand you cannot customize is checks. It will always compare just if you have the same reference.
Will these comparisons always return a boolean?
Because __eq__ can be re-implemented or overridden, it's not limited to return True or False. It could return anything (but in most cases it should return a boolean!).
For example with NumPy arrays the == will return an array:
>>> import numpy as np
>>> np.arange(10) == 2
array([False, False, True, False, False, False, False, False, False, False], dtype=bool)
But is checks will always return True or False!
1 As Aaron Hall mentioned in the comments:
Generally you shouldn't do any is True or is False checks because one normally uses these "checks" in a context that implicitly converts the condition to a boolean (for example in an if statement). So doing the is True comparison and the implicit boolean cast is doing more work than just doing the boolean cast - and you limit yourself to booleans (which isn't considered pythonic).
Like PEP8 mentions:
Don't compare boolean values to True or False using ==.
Yes: if greeting:
No: if greeting == True:
Worse: if greeting is True:
They are completely different. is checks for object identity, while == checks for equality (a notion that depends on the two operands' types).
It is only a lucky coincidence that "is" seems to work correctly with small integers (e.g. 5 == 4+1). That is because CPython optimizes the storage of integers in the range (-5 to 256) by making them singletons. This behavior is totally implementation-dependent and not guaranteed to be preserved under all manner of minor transformative operations.
For example, Python 3.5 also makes short strings singletons, but slicing them disrupts this behavior:
>>> "foo" + "bar" == "foobar"
True
>>> "foo" + "bar" is "foobar"
True
>>> "foo"[:] + "bar" == "foobar"
True
>>> "foo"[:] + "bar" is "foobar"
False
https://docs.python.org/library/stdtypes.html#comparisons
is tests for identity
== tests for equality
Each (small) integer value is mapped to a single value, so every 3 is identical and equal. This is an implementation detail, not part of the language spec though
Your answer is correct. The is operator compares the identity of two objects. The == operator compares the values of two objects.
An object's identity never changes once it has been created; you may think of it as the object's address in memory.
You can control comparison behaviour of object values by defining a __cmp__ method or a rich comparison method like __eq__.
Have a look at Stack Overflow question Python's “is” operator behaves unexpectedly with integers.
What it mostly boils down to is that "is" checks to see if they are the same object, not just equal to each other (the numbers below 256 are a special case).
In a nutshell, is checks whether two references point to the same object or not.== checks whether two objects have the same value or not.
a=[1,2,3]
b=a #a and b point to the same object
c=list(a) #c points to different object
if a==b:
print('#') #output:#
if a is b:
print('##') #output:##
if a==c:
print('###') #output:##
if a is c:
print('####') #no output as c and a point to different object
As the other people in this post answer the question in details the difference between == and is for comparing Objects or variables, I would emphasize mainly the comparison between is and == for strings which can give different results and I would urge programmers to carefully use them.
For string comparison, make sure to use == instead of is:
str = 'hello'
if (str is 'hello'):
print ('str is hello')
if (str == 'hello'):
print ('str == hello')
Out:
str is hello
str == hello
But in the below example == and is will get different results:
str2 = 'hello sam'
if (str2 is 'hello sam'):
print ('str2 is hello sam')
if (str2 == 'hello sam'):
print ('str2 == hello sam')
Out:
str2 == hello sam
Conclusion and Analysis:
Use is carefully to compare between strings.
Since is for comparing objects and since in Python 3+ every variable such as string interpret as an object, let's see what happened in above paragraphs.
In python there is id function that shows a unique constant of an object during its lifetime. This id is using in back-end of Python interpreter to compare two objects using is keyword.
str = 'hello'
id('hello')
> 140039832615152
id(str)
> 140039832615152
But
str2 = 'hello sam'
id('hello sam')
> 140039832615536
id(str2)
> 140039832615792
As John Feminella said, most of the time you will use == and != because your objective is to compare values. I'd just like to categorise what you would do the rest of the time:
There is one and only one instance of NoneType i.e. None is a singleton. Consequently foo == None and foo is None mean the same. However the is test is faster and the Pythonic convention is to use foo is None.
If you are doing some introspection or mucking about with garbage collection or checking whether your custom-built string interning gadget is working or suchlike, then you probably have a use-case for foo is bar.
True and False are also (now) singletons, but there is no use-case for foo == True and no use case for foo is True.
Most of them already answered to the point. Just as an additional note (based on my understanding and experimenting but not from a documented source), the statement
== if the objects referred to by the variables are equal
from above answers should be read as
== if the objects referred to by the variables are equal and objects belonging to the same type/class
. I arrived at this conclusion based on the below test:
list1 = [1,2,3,4]
tuple1 = (1,2,3,4)
print(list1)
print(tuple1)
print(id(list1))
print(id(tuple1))
print(list1 == tuple1)
print(list1 is tuple1)
Here the contents of the list and tuple are same but the type/class are different.
I doubt whether it is appropriate to use input variables in functions in cases where the input variable will always be the same.
mynum = int(datetime.today().weekday())
def somefuncion():
result = 3 + mynum
return result
optionA = somefuncion()
def somefunction2(number):
result = 3 + number
return result
optionB = somefunction2(mynum)
print(optionA == optionB) # true
Is there good practice in this respect ? or is it indifferent ?
In your case, it would probably be more appropriate to create constant variables.
MYNUM = 12
MYNUM_PLUS_TREE = MY_NUM + 3
That way, when you modify the value of MYNUM in your development, it automatically modifies the value of all the constant values that are generated from it (here it is MYNUM_PLUS_TREE).
The logic I apply here is the following: if a function always return the same value, then it is a "constant function", as in mathematics. Therefore, using a constant variable is preferable.
If you are sure that you are not going to modify the value of the variable anywhere in the code then you should declare it as a constant and use it that way within the code. i.e. not include it as an input variable.
Python, constants are capitalized (PEP 8 standards) which helps the programmer know it's a constant.
i.e
CONSTANT = "Whatever"
in your example:
MYNUM = int(datetime.today().weekday())
def somefuncion():
result = 3 + MYNUM
return result
This discussion of Verilog relational operators at ASIC World clearly has at least one mistake:
The result is a scalar value (example a < b)
0 if the relation is false (a is bigger then b)
1 if the relation is true (a is smaller then b)
x if any of the operands has unknown x bits (if a or b contains X)
Note: If any operand is x or z, then the result of that test is
treated as false (0)
Clearly, "a is bigger than b" should be "a is bigger than or equal to b".
There is something else that looks wrong to me, but I don't know if it's just because I'm a Verilog novice. The last bullet point seems to contradict the subsequent note, unless there is a difference between an operand having all unknown bits (in which case the result of a relational operator will be x) and an operand being x (in which case the result will be 0).
Is there a difference between an operand being x and all of its bits being X? I know Verilog is case-sensitive.
verilog is known for its x-propagation pessimism.
From lrm 11.4.4
An expression using these relational operators shall yield the scalar value 0 if the specified relation is false
or the value 1 if it is true. If either operand of a relational operator contains an unknown (x) or highimpedance
(z) value, then the result shall be a 1-bit unknown value (x).
so, if any of the values contains 'x' bits the result will be 'x'.
Now in case, when the result is used as a conditional expression, the if statement will take true brunch if and only if the result is '1'. Otherwise it will take the false branch. Also, there are conversion rules in verilog, where x and z values are converted to 0 in binary operations, which conditional operation is.
so, the comment on the site is correct, it is talking of the results of a test (as in if statement)
If any operand is x or z, then the result of that test is treated as false (0)
I think you should take your comments to the author of that website.
I take the statement inside the ()'s to be an example
1 if the relation is true (if for example, a is smaller then b)
The subsequent note refers to a more general issue not specific to relational operators. When you have
if (expression) true_statement; else false_statement;
When expression evaluates to an X or 0, the false_statement branch is taken.
Also, Verilog is not case sensitive about numeric literals. 'habcxz and 'hABCXZ are equivalent.
This question's answers are a community effort. Edit existing answers to improve this post. It is not currently accepting new answers or interactions.
My Google-fu has failed me.
In Python, are the following two tests for equality equivalent?
n = 5
# Test one.
if n == 5:
print 'Yay!'
# Test two.
if n is 5:
print 'Yay!'
Does this hold true for objects where you would be comparing instances (a list say)?
Okay, so this kind of answers my question:
L = []
L.append(1)
if L == [1]:
print 'Yay!'
# Holds true, but...
if L is [1]:
print 'Yay!'
# Doesn't.
So == tests value where is tests to see if they are the same object?
is will return True if two variables point to the same object (in memory), == if the objects referred to by the variables are equal.
>>> a = [1, 2, 3]
>>> b = a
>>> b is a
True
>>> b == a
True
# Make a new copy of list `a` via the slice operator,
# and assign it to variable `b`
>>> b = a[:]
>>> b is a
False
>>> b == a
True
In your case, the second test only works because Python caches small integer objects, which is an implementation detail. For larger integers, this does not work:
>>> 1000 is 10**3
False
>>> 1000 == 10**3
True
The same holds true for string literals:
>>> "a" is "a"
True
>>> "aa" is "a" * 2
True
>>> x = "a"
>>> "aa" is x * 2
False
>>> "aa" is intern(x*2)
True
Please see this question as well.
There is a simple rule of thumb to tell you when to use == or is.
== is for value equality. Use it when you would like to know if two objects have the same value.
is is for reference equality. Use it when you would like to know if two references refer to the same object.
In general, when you are comparing something to a simple type, you are usually checking for value equality, so you should use ==. For example, the intention of your example is probably to check whether x has a value equal to 2 (==), not whether x is literally referring to the same object as 2.
Something else to note: because of the way the CPython reference implementation works, you'll get unexpected and inconsistent results if you mistakenly use is to compare for reference equality on integers:
>>> a = 500
>>> b = 500
>>> a == b
True
>>> a is b
False
That's pretty much what we expected: a and b have the same value, but are distinct entities. But what about this?
>>> c = 200
>>> d = 200
>>> c == d
True
>>> c is d
True
This is inconsistent with the earlier result. What's going on here? It turns out the reference implementation of Python caches integer objects in the range -5..256 as singleton instances for performance reasons. Here's an example demonstrating this:
>>> for i in range(250, 260): a = i; print "%i: %s" % (i, a is int(str(i)));
...
250: True
251: True
252: True
253: True
254: True
255: True
256: True
257: False
258: False
259: False
This is another obvious reason not to use is: the behavior is left up to implementations when you're erroneously using it for value equality.
Is there a difference between == and is in Python?
Yes, they have a very important difference.
==: check for equality - the semantics are that equivalent objects (that aren't necessarily the same object) will test as equal. As the documentation says:
The operators <, >, ==, >=, <=, and != compare the values of two objects.
is: check for identity - the semantics are that the object (as held in memory) is the object. Again, the documentation says:
The operators is and is not test for object identity: x is y is true
if and only if x and y are the same object. Object identity is
determined using the id() function. x is not y yields the inverse
truth value.
Thus, the check for identity is the same as checking for the equality of the IDs of the objects. That is,
a is b
is the same as:
id(a) == id(b)
where id is the builtin function that returns an integer that "is guaranteed to be unique among simultaneously existing objects" (see help(id)) and where a and b are any arbitrary objects.
Other Usage Directions
You should use these comparisons for their semantics. Use is to check identity and == to check equality.
So in general, we use is to check for identity. This is usually useful when we are checking for an object that should only exist once in memory, referred to as a "singleton" in the documentation.
Use cases for is include:
None
enum values (when using Enums from the enum module)
usually modules
usually class objects resulting from class definitions
usually function objects resulting from function definitions
anything else that should only exist once in memory (all singletons, generally)
a specific object that you want by identity
Usual use cases for == include:
numbers, including integers
strings
lists
sets
dictionaries
custom mutable objects
other builtin immutable objects, in most cases
The general use case, again, for ==, is the object you want may not be the same object, instead it may be an equivalent one
PEP 8 directions
PEP 8, the official Python style guide for the standard library also mentions two use-cases for is:
Comparisons to singletons like None should always be done with is or
is not, never the equality operators.
Also, beware of writing if x when you really mean if x is not None --
e.g. when testing whether a variable or argument that defaults to None
was set to some other value. The other value might have a type (such
as a container) that could be false in a boolean context!
Inferring equality from identity
If is is true, equality can usually be inferred - logically, if an object is itself, then it should test as equivalent to itself.
In most cases this logic is true, but it relies on the implementation of the __eq__ special method. As the docs say,
The default behavior for equality comparison (== and !=) is based on
the identity of the objects. Hence, equality comparison of instances
with the same identity results in equality, and equality comparison of
instances with different identities results in inequality. A
motivation for this default behavior is the desire that all objects
should be reflexive (i.e. x is y implies x == y).
and in the interests of consistency, recommends:
Equality comparison should be reflexive. In other words, identical
objects should compare equal:
x is y implies x == y
We can see that this is the default behavior for custom objects:
>>> class Object(object): pass
>>> obj = Object()
>>> obj2 = Object()
>>> obj == obj, obj is obj
(True, True)
>>> obj == obj2, obj is obj2
(False, False)
The contrapositive is also usually true - if somethings test as not equal, you can usually infer that they are not the same object.
Since tests for equality can be customized, this inference does not always hold true for all types.
An exception
A notable exception is nan - it always tests as not equal to itself:
>>> nan = float('nan')
>>> nan
nan
>>> nan is nan
True
>>> nan == nan # !!!!!
False
Checking for identity can be much a much quicker check than checking for equality (which might require recursively checking members).
But it cannot be substituted for equality where you may find more than one object as equivalent.
Note that comparing equality of lists and tuples will assume that identity of objects are equal (because this is a fast check). This can create contradictions if the logic is inconsistent - as it is for nan:
>>> [nan] == [nan]
True
>>> (nan,) == (nan,)
True
A Cautionary Tale:
The question is attempting to use is to compare integers. You shouldn't assume that an instance of an integer is the same instance as one obtained by another reference. This story explains why.
A commenter had code that relied on the fact that small integers (-5 to 256 inclusive) are singletons in Python, instead of checking for equality.
Wow, this can lead to some insidious bugs. I had some code that checked if a is b, which worked as I wanted because a and b are typically small numbers. The bug only happened today, after six months in production, because a and b were finally large enough to not be cached. – gwg
It worked in development. It may have passed some unittests.
And it worked in production - until the code checked for an integer larger than 256, at which point it failed in production.
This is a production failure that could have been caught in code review or possibly with a style-checker.
Let me emphasize: do not use is to compare integers.
== determines if the values are equal, while is determines if they are the exact same object.
What's the difference between is and ==?
== and is are different comparison! As others already said:
== compares the values of the objects.
is compares the references of the objects.
In Python names refer to objects, for example in this case value1 and value2 refer to an int instance storing the value 1000:
value1 = 1000
value2 = value1
Because value2 refers to the same object is and == will give True:
>>> value1 == value2
True
>>> value1 is value2
True
In the following example the names value1 and value2 refer to different int instances, even if both store the same integer:
>>> value1 = 1000
>>> value2 = 1000
Because the same value (integer) is stored == will be True, that's why it's often called "value comparison". However is will return False because these are different objects:
>>> value1 == value2
True
>>> value1 is value2
False
When to use which?
Generally is is a much faster comparison. That's why CPython caches (or maybe reuses would be the better term) certain objects like small integers, some strings, etc. But this should be treated as implementation detail that could (even if unlikely) change at any point without warning.
You should only use is if you:
want to check if two objects are really the same object (not just the same "value"). One example can be if you use a singleton object as constant.
want to compare a value to a Python constant. The constants in Python are:
None
True1
False1
NotImplemented
Ellipsis
__debug__
classes (for example int is int or int is float)
there could be additional constants in built-in modules or 3rd party modules. For example np.ma.masked from the NumPy module)
In every other case you should use == to check for equality.
Can I customize the behavior?
There is some aspect to == that hasn't been mentioned already in the other answers: It's part of Pythons "Data model". That means its behavior can be customized using the __eq__ method. For example:
class MyClass(object):
def __init__(self, val):
self._value = val
def __eq__(self, other):
print('__eq__ method called')
try:
return self._value == other._value
except AttributeError:
raise TypeError('Cannot compare {0} to objects of type {1}'
.format(type(self), type(other)))
This is just an artificial example to illustrate that the method is really called:
>>> MyClass(10) == MyClass(10)
__eq__ method called
True
Note that by default (if no other implementation of __eq__ can be found in the class or the superclasses) __eq__ uses is:
class AClass(object):
def __init__(self, value):
self._value = value
>>> a = AClass(10)
>>> b = AClass(10)
>>> a == b
False
>>> a == a
So it's actually important to implement __eq__ if you want "more" than just reference-comparison for custom classes!
On the other hand you cannot customize is checks. It will always compare just if you have the same reference.
Will these comparisons always return a boolean?
Because __eq__ can be re-implemented or overridden, it's not limited to return True or False. It could return anything (but in most cases it should return a boolean!).
For example with NumPy arrays the == will return an array:
>>> import numpy as np
>>> np.arange(10) == 2
array([False, False, True, False, False, False, False, False, False, False], dtype=bool)
But is checks will always return True or False!
1 As Aaron Hall mentioned in the comments:
Generally you shouldn't do any is True or is False checks because one normally uses these "checks" in a context that implicitly converts the condition to a boolean (for example in an if statement). So doing the is True comparison and the implicit boolean cast is doing more work than just doing the boolean cast - and you limit yourself to booleans (which isn't considered pythonic).
Like PEP8 mentions:
Don't compare boolean values to True or False using ==.
Yes: if greeting:
No: if greeting == True:
Worse: if greeting is True:
They are completely different. is checks for object identity, while == checks for equality (a notion that depends on the two operands' types).
It is only a lucky coincidence that "is" seems to work correctly with small integers (e.g. 5 == 4+1). That is because CPython optimizes the storage of integers in the range (-5 to 256) by making them singletons. This behavior is totally implementation-dependent and not guaranteed to be preserved under all manner of minor transformative operations.
For example, Python 3.5 also makes short strings singletons, but slicing them disrupts this behavior:
>>> "foo" + "bar" == "foobar"
True
>>> "foo" + "bar" is "foobar"
True
>>> "foo"[:] + "bar" == "foobar"
True
>>> "foo"[:] + "bar" is "foobar"
False
https://docs.python.org/library/stdtypes.html#comparisons
is tests for identity
== tests for equality
Each (small) integer value is mapped to a single value, so every 3 is identical and equal. This is an implementation detail, not part of the language spec though
Your answer is correct. The is operator compares the identity of two objects. The == operator compares the values of two objects.
An object's identity never changes once it has been created; you may think of it as the object's address in memory.
You can control comparison behaviour of object values by defining a __cmp__ method or a rich comparison method like __eq__.
Have a look at Stack Overflow question Python's “is” operator behaves unexpectedly with integers.
What it mostly boils down to is that "is" checks to see if they are the same object, not just equal to each other (the numbers below 256 are a special case).
In a nutshell, is checks whether two references point to the same object or not.== checks whether two objects have the same value or not.
a=[1,2,3]
b=a #a and b point to the same object
c=list(a) #c points to different object
if a==b:
print('#') #output:#
if a is b:
print('##') #output:##
if a==c:
print('###') #output:##
if a is c:
print('####') #no output as c and a point to different object
As the other people in this post answer the question in details the difference between == and is for comparing Objects or variables, I would emphasize mainly the comparison between is and == for strings which can give different results and I would urge programmers to carefully use them.
For string comparison, make sure to use == instead of is:
str = 'hello'
if (str is 'hello'):
print ('str is hello')
if (str == 'hello'):
print ('str == hello')
Out:
str is hello
str == hello
But in the below example == and is will get different results:
str2 = 'hello sam'
if (str2 is 'hello sam'):
print ('str2 is hello sam')
if (str2 == 'hello sam'):
print ('str2 == hello sam')
Out:
str2 == hello sam
Conclusion and Analysis:
Use is carefully to compare between strings.
Since is for comparing objects and since in Python 3+ every variable such as string interpret as an object, let's see what happened in above paragraphs.
In python there is id function that shows a unique constant of an object during its lifetime. This id is using in back-end of Python interpreter to compare two objects using is keyword.
str = 'hello'
id('hello')
> 140039832615152
id(str)
> 140039832615152
But
str2 = 'hello sam'
id('hello sam')
> 140039832615536
id(str2)
> 140039832615792
As John Feminella said, most of the time you will use == and != because your objective is to compare values. I'd just like to categorise what you would do the rest of the time:
There is one and only one instance of NoneType i.e. None is a singleton. Consequently foo == None and foo is None mean the same. However the is test is faster and the Pythonic convention is to use foo is None.
If you are doing some introspection or mucking about with garbage collection or checking whether your custom-built string interning gadget is working or suchlike, then you probably have a use-case for foo is bar.
True and False are also (now) singletons, but there is no use-case for foo == True and no use case for foo is True.
Most of them already answered to the point. Just as an additional note (based on my understanding and experimenting but not from a documented source), the statement
== if the objects referred to by the variables are equal
from above answers should be read as
== if the objects referred to by the variables are equal and objects belonging to the same type/class
. I arrived at this conclusion based on the below test:
list1 = [1,2,3,4]
tuple1 = (1,2,3,4)
print(list1)
print(tuple1)
print(id(list1))
print(id(tuple1))
print(list1 == tuple1)
print(list1 is tuple1)
Here the contents of the list and tuple are same but the type/class are different.