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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.
Application
I want to create a python function (e.g., Laplacian(expr)). The Laplacian operator is defined as taking the sum of the second partial derivatives of expr with respect to each variable (e.g., Laplacian(f(x,y,z)) is diff(f,x,x) + diff(f,y,y) + diff(f,z,z). In the expression, there may be arbitrary constants c,k, etc that are not variables as far as the expression is concerned. Just as you cannot take the derivative diff(f,126), taking the derivative of the expression with respect to c is not defined.
Need I need to be able to extract the non-constant free symbols from an expression.
Problem
Though I can construct c = Symbol('c', constant=True, number=True) in Sympy, c.is_constant() evaluates to False. Similarly, g(c).is_constant() evaluates to false. For my application, the symbol c should have the exact same behavior as E.is_constant() == True and g(E).is_constant() == True, as it is a number.
Caveats
I cannot register c as a singleton, as it is only defined with respect to this particular proof or expression.
I cannot construct it in the same way values like E are constructed, as there is no specific numeric value for it to be assigned to.
I cannot simply add a constants keyword to Laplacian, as I do not know all such constants that may appear (just as it would not make sense to add constants=[1,2,3,4,...] to solve()).
I cannot simply add a variables keyword to Laplacian, as I do not know the variables that appear in the expression.
The desired usage is as follows:
>>> C = ... # somehow create the constant
>>> symbols_that_arent_constant_numbers(g(C))
set()
>>> symbols_that_arent_constant_numbers(g(C, x))
{x}
>>> g(C).is_constant()
True
stretch goals: It would be awesome to have an arbitrary constant symbol that absorbs other constant terms in the same way that constantsimp operates. Consider introducing an integration constant c into an expression, and then multiplying that expression by I. As far as we are concerned algebraically, cI=c without loosing any generality.
Note
Per Oscar Benjamin's comments on question, current best practices when constructing a sympy-style method (like Laplacian) is to pass a constants or variables keyword into a method. Bare that in mind when applying the following solution. Furthermore, free_symbols has many applications within Sympy, so using another class that has established semantics may have unexpected side-effects.
(I am not accepting my own solution in the event that a better one comes along, as Mr. Benjamin has pointed out there are many open related issues.)
Solution
Sympy provides a mechanism to create such a constant: sympy.physics.units.quantities.Quantity. Its behavior is equivalent to Symbol and singleton constants, but most notably it does not appear as a free symbol. This can help prevent code from interpreting it as a variable that may be differentiated, etc.
from sympy.physics.units.quantities import Quantity
C = Quantity('C')
print("C constant? : ", C.is_constant())
print("C free symbols : ", C.free_symbols)
print("x constant? : ", x.is_constant())
print("g(C) constant? : ", g(C).is_constant())
print("g(x) constant? : ", g(x).is_constant())
print("g(C,x) constant : ", g(C,x).is_constant())
print("g(C) free symbols : ", g(C).free_symbols)
print("g(C,x) free symbols: ", g(C,x).free_symbols)
assert C.is_constant()
assert C.free_symbols == set([])
assert g(C).is_constant()
assert g(C, x).is_constant() == g(x).is_constant() # consistent interface
assert g(C).free_symbols == set([])
assert g(C, x).free_symbols == set([x])
assert [5/C] == solve(C*x -5, x)
The above snippet produces the following output when tested in sympy==1.5.1:
C constant? : True
C free symbols : set()
x constant? : False
g(C) constant? : True
g(x) constant? : None
g(C,x) constant : None
g(C) free symbols : set()
g(C,x) free symbols: {x}
Note that while g(C).is_constant()==True, we see that g(x).is_constant() == None, as well as g(C,x).is_constant() == None. Consequently, I only assert that those two applications have a consistent interface.
The below module declares a set of numbers that are in the range 10 to 99 that are divisible by 2 only once and call it NumbersThatDivideBy2Once. At the end it declares a theorem that the constant input is a subset of NumbersThatDivideBy2Once.
--------------------------- MODULE TestModule ---------------------------
EXTENDS Naturals
CONSTANT input
Numbers == { n \in Nat : n > 9 /\ n < 100 }
DividesBy2(n) == (n % 2) = 0
DividesBy2Once(n) == DividesBy2(n) /\ ~DividesBy2(n \div 2)
NumbersThatDivideBy2Once == { n \in Numbers: DividesBy2Once(n) }
THEOREM input \subseteq NumbersThatDivideBy2Once
=======================
How can I check if this theorem holds for a given input? If I run a model check with a provided set of numbers as input, even if some of those numbers are not part of NumbersThatDivideBy2Once I still get no errors.
Give your theorem a name,
THEOREM T == input \subseteq NumbersThatDivideBy2Once
Go to the "Model Checking Results" tab, and in "Evaluate Constant Expression" introduce T, in order for it to be evaluated.
Your model checker needs to be told what to do with the specification file, which is essentially a just collection of mathematical definitions.
In "normal use" you want to provide TLC a temporal formula representing your specification (usually given the nameSpec in the specification file). You introduce it in the "model overview" tab, under "what is the behaviour spec?". And that is what TLC uses to perform model checking.
In this case you don't have that. So just keep the option "no behaviour spec" and, as indicated above, specify in the "Model Checking Results" tab the constant expression you want to evaluate.
I feel really stupid, and feel like I'm missing something.
I've basically got two files:
module.pl for the universal logic rules (meant to be reusable)
state.pl one for the current scenario
In the module file (module.pl) I've declared:
inside(Food,Eater,T) :-
isTime(T),
injestEvent(InjEvent),
justAfter(T,InjEvent),
actorOfEvent(InjEvent, Eater),
objectOfEvent(InjEvent, Food).
Q1) I've had to declare all those other predicates with singleton variables (in the same file), just to stop module.pl complaining they don't exist:
isTime(_T).
justAfter(_Time,_Event).
actorOfEvent(_Event, _ActorOfEvent).
objectOfEvent(_Event,_ActorOfEvent).
Is that right?
Q2) I can't use those predicates like justAfter/2 my other file without it saying:
Local definition of user:justAfter/2 overrides weak import from module
How can I use the predicates I've imported from my module, rather redefining it?
Prolog modules were designed to hide auxiliary predicates. They don't provide a concept of interface that allows separating predicate declarations from predicate definitions. That's why the compiler complains if you export predicates that are not defined. From your description, I assume you tried something like:
----- module.pl -----
:- module(module, [
inside/3, isTime/1, injestEvent/1, justAfter/2, actorOfEvent/2, objectOfEvent/2
]).
inside(Food,Eater,T) :-
isTime(T),
injestEvent(InjEvent),
justAfter(T,InjEvent),
actorOfEvent(InjEvent, Eater),
objectOfEvent(InjEvent, Food).
---------------------
which results in:
?- [module].
ERROR: Exported procedure module:justAfter/2 is not defined
ERROR: Exported procedure module:isTime/1 is not defined
ERROR: Exported procedure module:injestEvent/1 is not defined
ERROR: Exported procedure module:objectOfEvent/2 is not defined
ERROR: Exported procedure module:actorOfEvent/2 is not defined
true.
You attempted to workaround this error by adding local definitions. But this just result in the second problem you describe. When you do something like:
?- use_module(module).
You import all the predicates exported by module, including those that you want to define in state.pl. Therefore, the compiler warns you, when loading state.pl, that this file is overriding those predicates. E.g. with:
----- state.pl -----
isTime(1).
injestEvent(injEvent).
justAfter(1, injEvent).
actorOfEvent(injEvent, eater).
objectOfEvent(injEvent, food).
--------------------
we get:
?- [state].
Warning: /Users/pmoura/Desktop/state.pl:1:
Local definition of user:isTime/1 overrides weak import from module
Warning: /Users/pmoura/Desktop/state.pl:2:
Local definition of user:injestEvent/1 overrides weak import from module
Warning: /Users/pmoura/Desktop/state.pl:3:
Local definition of user:justAfter/2 overrides weak import from module
Warning: /Users/pmoura/Desktop/state.pl:4:
Local definition of user:actorOfEvent/2 overrides weak import from module
Warning: /Users/pmoura/Desktop/state.pl:5:
Local definition of user:objectOfEvent/2 overrides weak import from module
true.
Although these are warnings and not errors, calling the inside/3 predicate will not give you what you want:
?- inside(Food,Eater,T).
true.
Where are the bindings?!? Let's trace the call to highlight the cause:
?- trace.
true.
[trace] ?- inside(Food,Eater,T).
Call: (8) module:inside(_2508, _2510, _2512) ? creep
Call: (9) module:isTime(_2512) ? creep
Exit: (9) module:isTime(_2512) ? creep
Call: (9) module:injestEvent(_2804) ? creep
Exit: (9) module:injestEvent(_2804) ? creep
Call: (9) module:justAfter(_2512, _2806) ? creep
Exit: (9) module:justAfter(_2512, _2806) ? creep
Call: (9) module:actorOfEvent(_2804, _2510) ? creep
Exit: (9) module:actorOfEvent(_2804, _2510) ? creep
Call: (9) module:objectOfEvent(_2804, _2508) ? creep
Exit: (9) module:objectOfEvent(_2804, _2508) ? creep
Exit: (8) module:inside(_2508, _2510, _2512) ? creep
true.
The trace makes it clear that the "state" predicates are being called in the wrong context.
A clean solution is to use Logtalk objects instead of Prolog modules. Logtalk extends Prolog and supports most systems, including SWI-Prolog. It supports interfaces/protocols as first-class entities (which solve the first problem you mention) and supports inheritance and calling predicates in their usage context (which solves the second problem). You could use e.g.
----- common.lgt -----
:- object(common).
:- public([
inside/3, isTime/1, injestEvent/1, justAfter/2, actorOfEvent/2, objectOfEvent/2
]).
inside(Food,Eater,T) :-
% call the next predicates in "self", i.e. in the
% object that received the inside/3 message
::isTime(T),
::injestEvent(InjEvent),
::justAfter(T,InjEvent),
::actorOfEvent(InjEvent, Eater),
::objectOfEvent(InjEvent, Food).
:- end_object.
----------------------
and then represent "state" as:
----- state.lgt -----
:- object(state, extends(common)).
isTime(1).
injestEvent(injEvent).
justAfter(1, injEvent).
actorOfEvent(injEvent, eater).
objectOfEvent(injEvent, food).
:- end_object.
---------------------
A quick test (after installing Logtalk):
$ swilgt
...
?- {common, state}.
...
true.
?- state::inside(Food,Eater,T).
Food = food,
Eater = eater,
T = 1.
As a bonus, you can define as many "state" objects as you need. You can also have default definitions for the "state" predicates in the common object. These will be inherited and used when the "state" objects don't provide a definition for a particular predicate. For example, let's add to common the clause:
objectOfEvent(injEvent, drink).
and delete (or comment out) the clause objectOfEvent(injEvent, food). from state. Save and reload and retrying the query will give you:
?- {*}. % abbreviation for Logtalk's make
% Redefining object common
...
% Redefining object state
...
true.
?- state::inside(Food,Eater,T).
Food = drink,
Eater = eater,
T = 1.
If needed, you can also dynamically create new state objects instead of defining them in source files. For example:
?- create_object(s2, [extends(common)], [], [isTime(42), ...]).
This may not be the answer you were looking for but this is also the case where the best answer is to use the right tool^H^H^H^H encapsulation mechanism for the job. Your programming pattern is also a quite common one (and one of the reasons Logtalk was developed).
It's very simple to add a basic form of 'object orientation'.
Let's say we have a clause in a module logic:
:- module(logic, [inside/4]).
% apply the rule to a specified module (expected to be a state object)
inside(M,Food,Eater,T) :-
M:isTime(T),
M:injestEvent(InjEvent),
M:justAfter(T, InjEvent),
M:actorOfEvent(InjEvent, Eater),
M:objectOfEvent(InjEvent, Food).
and we have a lot of state objects pertinent: in a file state1.pl
isTime(10).
injestEvent(eat).
justAfter(10, eat).
actorOfEvent(eat, mick).
objectOfEvent(eat, food).
and in a file state2.pl
isTime(20).
injestEvent(sleep).
justAfter(20, sleep).
actorOfEvent(sleep, everyone).
objectOfEvent(sleep, dream).
then a possible session:
?- [logic].
true.
?- s1:consult(state1).
true.
?- s2:consult(state2).
true.
?- inside(s1,Food,Eater,T).
Food = food,
Eater = mick,
T = 10.
?- inside(s2,What,Who,T).
What = dream,
Who = everyone,
T = 20.
A small generalization, worth to try:
inside(M,Food,Eater,T) :-
resolve(M),
M:isTime(T),
...
where resolve/1 could be
resolve(M) :- var(M) -> current_module(M), catch(M:isTime(_),_,fail) ; true.
this trick enable 'browsing the objects':
?- inside(M,X,Y,Z).
M = s2,
X = dream,
Y = everyone,
Z = 20 ;
M = s1,
X = food,
Y = mick,
Z = 10 ;
false.
An alternative to CapelliC is the use of Prolog dicts. They have been introduced by SWI-Prolog and since release 1.3.0 they are also available in Jekejeke Prolog. If the receiver is not needed, one can simply use an underscore.
File state1.pl:
:- module(state1, [isTime/2, injestEvent/2, justAfter/3,
actorOfEvent/3, objectOfEvent/3]).
:- reexport(logic).
_.isTime() := 10.
_.injestEvent() := eat.
_.justAfter(10) := eat.
_.actorOfEvent(eat) := mick.
_.objectOfEvent(eat) := food.
File state2.pl:
:- module(state2, [isTime/2, injestEvent/2, justAfter/3,
actorOfEvent/3, objectOfEvent/3]).
:- reexport(logic).
_.isTime() := 20.
_.injestEvent() := sleep.
_.justAfter(20) := sleep.
_.actorOfEvent(sleep) := everyone.
_.objectOfEvent(sleep) := dream.
File logic.pl:
:- module(logic, [inside/4]).
M.inside(Food,Eater) := T :-
T = M.isTime(),
InjEvent = M.injestEvent(),
InjEvent = M.justAfter(T),
Eater = M.actorOfEvent(InjEvent),
Food = M.objectOfEvent(InjEvent).
To make the logic also visible in state1 and state2 use reexport/1. This allows sending a message to state1 or state2, but nevertheless a method from logic will be processed. Here is an example run:
Welcome to SWI-Prolog (threaded, 64 bits, version 7.7.19)
SWI-Prolog comes with ABSOLUTELY NO WARRANTY. This is free software.
?- T = state1{}.inside(Food,Eater).
T = 10,
Food = food,
Eater = mick.
?- T = state2{}.inside(Food,Eater).
T = 20,
Food = dream,
Eater = everyone.
The exports of isTime/2, injestEvent/2, etc.. will go away with the upcoming release 1.3.1 of Jekejeke Prolog when we have made ('.')/3 call-site aware. But the result for Jekejeke Prolog is the same:
Jekejeke Prolog 3, Runtime Library 1.3.0
(c) 1985-2018, XLOG Technologies GmbH, Switzerland
?- T = state1{}.inside(Food,Eater).
T = 10,
Food = food,
Eater = mick
?- T = state2{}.inside(Food,Eater).
T = 20,
Food = dream,
Eater = everyone
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.