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" is " returns False even if strings are same in Python [duplicate]

Two string variables are set to the same value. s1 == s2 always returns True, but s1 is s2 sometimes returns False.
If I open my Python interpreter and do the same is comparison, it succeeds:
>>> s1 = 'text'
>>> s2 = 'text'
>>> s1 is s2
True
Why is this?

is is identity testing, and == is equality testing. What happens in your code would be emulated in the interpreter like this:
>>> a = 'pub'
>>> b = ''.join(['p', 'u', 'b'])
>>> a == b
True
>>> a is b
False
So, no wonder they're not the same, right?
In other words: a is b is the equivalent of id(a) == id(b)

Other answers here are correct: is is used for identity comparison, while == is used for equality comparison. Since what you care about is equality (the two strings should contain the same characters), in this case the is operator is simply wrong and you should be using == instead.
The reason is works interactively is that (most) string literals are interned by default. From Wikipedia:
Interned strings speed up string
comparisons, which are sometimes a
performance bottleneck in applications
(such as compilers and dynamic
programming language runtimes) that
rely heavily on hash tables with
string keys. Without interning,
checking that two different strings
are equal involves examining every
character of both strings. This is
slow for several reasons: it is
inherently O(n) in the length of the
strings; it typically requires reads
from several regions of memory, which
take time; and the reads fills up the
processor cache, meaning there is less
cache available for other needs. With
interned strings, a simple object
identity test suffices after the
original intern operation; this is
typically implemented as a pointer
equality test, normally just a single
machine instruction with no memory
reference at all.
So, when you have two string literals (words that are literally typed into your program source code, surrounded by quotation marks) in your program that have the same value, the Python compiler will automatically intern the strings, making them both stored at the same memory location. (Note that this doesn't always happen, and the rules for when this happens are quite convoluted, so please don't rely on this behavior in production code!)
Since in your interactive session both strings are actually stored in the same memory location, they have the same identity, so the is operator works as expected. But if you construct a string by some other method (even if that string contains exactly the same characters), then the string may be equal, but it is not the same string -- that is, it has a different identity, because it is stored in a different place in memory.

The is keyword is a test for object identity while == is a value comparison.
If you use is, the result will be true if and only if the object is the same object. However, == will be true any time the values of the object are the same.

One last thing to note is you may use the sys.intern function to ensure that you're getting a reference to the same string:
>>> from sys import intern
>>> a = intern('a')
>>> a2 = intern('a')
>>> a is a2
True
As pointed out in previous answers, you should not be using is to determine equality of strings. But this may be helpful to know if you have some kind of weird requirement to use is.
Note that the intern function used to be a built-in on Python 2, but it was moved to the sys module in Python 3.

is is identity testing and == is equality testing. This means is is a way to check whether two things are the same things, or just equivalent.
Say you've got a simple person object. If it is named 'Jack' and is '23' years old, it's equivalent to another 23-year-old Jack, but it's not the same person.
class Person(object):
def __init__(self, name, age):
self.name = name
self.age = age
def __eq__(self, other):
return self.name == other.name and self.age == other.age
jack1 = Person('Jack', 23)
jack2 = Person('Jack', 23)
jack1 == jack2 # True
jack1 is jack2 # False
They're the same age, but they're not the same instance of person. A string might be equivalent to another, but it's not the same object.

This is a side note, but in idiomatic Python, you will often see things like:
if x is None:
# Some clauses
This is safe, because there is guaranteed to be one instance of the Null Object (i.e., None).

If you're not sure what you're doing, use the '=='.
If you have a little more knowledge about it you can use 'is' for known objects like 'None'.
Otherwise, you'll end up wondering why things doesn't work and why this happens:
>>> a = 1
>>> b = 1
>>> b is a
True
>>> a = 6000
>>> b = 6000
>>> b is a
False
I'm not even sure if some things are guaranteed to stay the same between different Python versions/implementations.

From my limited experience with Python, is is used to compare two objects to see if they are the same object as opposed to two different objects with the same value. == is used to determine if the values are identical.
Here is a good example:
>>> s1 = u'public'
>>> s2 = 'public'
>>> s1 is s2
False
>>> s1 == s2
True
s1 is a Unicode string, and s2 is a normal string. They are not the same type, but they are the same value.

I think it has to do with the fact that, when the 'is' comparison evaluates to false, two distinct objects are used. If it evaluates to true, that means internally it's using the same exact object and not creating a new one, possibly because you created them within a fraction of 2 or so seconds and because there isn't a large time gap in between it's optimized and uses the same object.
This is why you should be using the equality operator ==, not is, to compare the value of a string object.
>>> s = 'one'
>>> s2 = 'two'
>>> s is s2
False
>>> s2 = s2.replace('two', 'one')
>>> s2
'one'
>>> s2 is s
False
>>>
In this example, I made s2, which was a different string object previously equal to 'one' but it is not the same object as s, because the interpreter did not use the same object as I did not initially assign it to 'one', if I had it would have made them the same object.

The == operator tests value equivalence. The is operator tests object identity, and Python tests whether the two are really the same object (i.e., live at the same address in memory).
>>> a = 'banana'
>>> b = 'banana'
>>> a is b
True
In this example, Python only created one string object, and both a and b refers to it. The reason is that Python internally caches and reuses some strings as an optimization. There really is just a string 'banana' in memory, shared by a and b. To trigger the normal behavior, you need to use longer strings:
>>> a = 'a longer banana'
>>> b = 'a longer banana'
>>> a == b, a is b
(True, False)
When you create two lists, you get two objects:
>>> a = [1, 2, 3]
>>> b = [1, 2, 3]
>>> a is b
False
In this case we would say that the two lists are equivalent, because they have the same elements, but not identical, because they are not the same object. If two objects are identical, they are also equivalent, but if they are equivalent, they are not necessarily identical.
If a refers to an object and you assign b = a, then both variables refer to the same object:
>>> a = [1, 2, 3]
>>> b = a
>>> b is a
True
Reference: Think Python 2e by Allen B. Downey

I believe that this is known as "interned" strings. Python does this, so does Java, and so do C and C++ when compiling in optimized modes.
If you use two identical strings, instead of wasting memory by creating two string objects, all interned strings with the same contents point to the same memory.
This results in the Python "is" operator returning True because two strings with the same contents are pointing at the same string object. This will also happen in Java and in C.
This is only useful for memory savings though. You cannot rely on it to test for string equality, because the various interpreters and compilers and JIT engines cannot always do it.

Actually, the is operator checks for identity and == operator checks for equality.
From the language reference:
Types affect almost all aspects of object behavior. Even the importance of object identity is affected in some sense: for immutable types, operations that compute new values may actually return a reference to any existing object with the same type and value, while for mutable objects this is not allowed. E.g., after a = 1; b = 1, a and b may or may not refer to the same object with the value one, depending on the implementation, but after c = []; d = [], c and d are guaranteed to refer to two different, unique, newly created empty lists. (Note that c = d = [] assigns the same object to both c and d.)
So from the above statement we can infer that the strings, which are immutable types, may fail when checked with "is" and may succeed when checked with "is".
The same applies for int and tuple which are also immutable types.

is will compare the memory location. It is used for object-level comparison.
== will compare the variables in the program. It is used for checking at a value level.
is checks for address level equivalence
== checks for value level equivalence

is is identity testing and == is equality testing (see the Python documentation).
In most cases, if a is b, then a == b. But there are exceptions, for example:
>>> nan = float('nan')
>>> nan is nan
True
>>> nan == nan
False
So, you can only use is for identity tests, never equality tests.

The basic concept, we have to be clear, while approaching this question, is to understand the difference between is and ==.
"is" is will compare the memory location. if id(a)==id(b), then a is b returns true else it returns false.
So, we can say that is is used for comparing memory locations. Whereas,
== is used for equality testing which means that it just compares only the resultant values. The below shown code may acts as an example to the above given theory.
Code
In the case of string literals (strings without getting assigned to variables), the memory address will be same as shown in the picture. so, id(a)==id(b). The remaining of this is self-explanatory.

Python integer objects [duplicate]

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.

Broken input method [duplicate]

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.

If statement string comparison in python [duplicate]

Two string variables are set to the same value. s1 == s2 always returns True, but s1 is s2 sometimes returns False.
If I open my Python interpreter and do the same is comparison, it succeeds:
>>> s1 = 'text'
>>> s2 = 'text'
>>> s1 is s2
True
Why is this?

is is identity testing, and == is equality testing. What happens in your code would be emulated in the interpreter like this:
>>> a = 'pub'
>>> b = ''.join(['p', 'u', 'b'])
>>> a == b
True
>>> a is b
False
So, no wonder they're not the same, right?
In other words: a is b is the equivalent of id(a) == id(b)

Other answers here are correct: is is used for identity comparison, while == is used for equality comparison. Since what you care about is equality (the two strings should contain the same characters), in this case the is operator is simply wrong and you should be using == instead.
The reason is works interactively is that (most) string literals are interned by default. From Wikipedia:
Interned strings speed up string
comparisons, which are sometimes a
performance bottleneck in applications
(such as compilers and dynamic
programming language runtimes) that
rely heavily on hash tables with
string keys. Without interning,
checking that two different strings
are equal involves examining every
character of both strings. This is
slow for several reasons: it is
inherently O(n) in the length of the
strings; it typically requires reads
from several regions of memory, which
take time; and the reads fills up the
processor cache, meaning there is less
cache available for other needs. With
interned strings, a simple object
identity test suffices after the
original intern operation; this is
typically implemented as a pointer
equality test, normally just a single
machine instruction with no memory
reference at all.
So, when you have two string literals (words that are literally typed into your program source code, surrounded by quotation marks) in your program that have the same value, the Python compiler will automatically intern the strings, making them both stored at the same memory location. (Note that this doesn't always happen, and the rules for when this happens are quite convoluted, so please don't rely on this behavior in production code!)
Since in your interactive session both strings are actually stored in the same memory location, they have the same identity, so the is operator works as expected. But if you construct a string by some other method (even if that string contains exactly the same characters), then the string may be equal, but it is not the same string -- that is, it has a different identity, because it is stored in a different place in memory.

The is keyword is a test for object identity while == is a value comparison.
If you use is, the result will be true if and only if the object is the same object. However, == will be true any time the values of the object are the same.

One last thing to note is you may use the sys.intern function to ensure that you're getting a reference to the same string:
>>> from sys import intern
>>> a = intern('a')
>>> a2 = intern('a')
>>> a is a2
True
As pointed out in previous answers, you should not be using is to determine equality of strings. But this may be helpful to know if you have some kind of weird requirement to use is.
Note that the intern function used to be a built-in on Python 2, but it was moved to the sys module in Python 3.

is is identity testing and == is equality testing. This means is is a way to check whether two things are the same things, or just equivalent.
Say you've got a simple person object. If it is named 'Jack' and is '23' years old, it's equivalent to another 23-year-old Jack, but it's not the same person.
class Person(object):
def __init__(self, name, age):
self.name = name
self.age = age
def __eq__(self, other):
return self.name == other.name and self.age == other.age
jack1 = Person('Jack', 23)
jack2 = Person('Jack', 23)
jack1 == jack2 # True
jack1 is jack2 # False
They're the same age, but they're not the same instance of person. A string might be equivalent to another, but it's not the same object.

This is a side note, but in idiomatic Python, you will often see things like:
if x is None:
# Some clauses
This is safe, because there is guaranteed to be one instance of the Null Object (i.e., None).

If you're not sure what you're doing, use the '=='.
If you have a little more knowledge about it you can use 'is' for known objects like 'None'.
Otherwise, you'll end up wondering why things doesn't work and why this happens:
>>> a = 1
>>> b = 1
>>> b is a
True
>>> a = 6000
>>> b = 6000
>>> b is a
False
I'm not even sure if some things are guaranteed to stay the same between different Python versions/implementations.

From my limited experience with Python, is is used to compare two objects to see if they are the same object as opposed to two different objects with the same value. == is used to determine if the values are identical.
Here is a good example:
>>> s1 = u'public'
>>> s2 = 'public'
>>> s1 is s2
False
>>> s1 == s2
True
s1 is a Unicode string, and s2 is a normal string. They are not the same type, but they are the same value.

I think it has to do with the fact that, when the 'is' comparison evaluates to false, two distinct objects are used. If it evaluates to true, that means internally it's using the same exact object and not creating a new one, possibly because you created them within a fraction of 2 or so seconds and because there isn't a large time gap in between it's optimized and uses the same object.
This is why you should be using the equality operator ==, not is, to compare the value of a string object.
>>> s = 'one'
>>> s2 = 'two'
>>> s is s2
False
>>> s2 = s2.replace('two', 'one')
>>> s2
'one'
>>> s2 is s
False
>>>
In this example, I made s2, which was a different string object previously equal to 'one' but it is not the same object as s, because the interpreter did not use the same object as I did not initially assign it to 'one', if I had it would have made them the same object.

The == operator tests value equivalence. The is operator tests object identity, and Python tests whether the two are really the same object (i.e., live at the same address in memory).
>>> a = 'banana'
>>> b = 'banana'
>>> a is b
True
In this example, Python only created one string object, and both a and b refers to it. The reason is that Python internally caches and reuses some strings as an optimization. There really is just a string 'banana' in memory, shared by a and b. To trigger the normal behavior, you need to use longer strings:
>>> a = 'a longer banana'
>>> b = 'a longer banana'
>>> a == b, a is b
(True, False)
When you create two lists, you get two objects:
>>> a = [1, 2, 3]
>>> b = [1, 2, 3]
>>> a is b
False
In this case we would say that the two lists are equivalent, because they have the same elements, but not identical, because they are not the same object. If two objects are identical, they are also equivalent, but if they are equivalent, they are not necessarily identical.
If a refers to an object and you assign b = a, then both variables refer to the same object:
>>> a = [1, 2, 3]
>>> b = a
>>> b is a
True
Reference: Think Python 2e by Allen B. Downey

I believe that this is known as "interned" strings. Python does this, so does Java, and so do C and C++ when compiling in optimized modes.
If you use two identical strings, instead of wasting memory by creating two string objects, all interned strings with the same contents point to the same memory.
This results in the Python "is" operator returning True because two strings with the same contents are pointing at the same string object. This will also happen in Java and in C.
This is only useful for memory savings though. You cannot rely on it to test for string equality, because the various interpreters and compilers and JIT engines cannot always do it.

Actually, the is operator checks for identity and == operator checks for equality.
From the language reference:
Types affect almost all aspects of object behavior. Even the importance of object identity is affected in some sense: for immutable types, operations that compute new values may actually return a reference to any existing object with the same type and value, while for mutable objects this is not allowed. E.g., after a = 1; b = 1, a and b may or may not refer to the same object with the value one, depending on the implementation, but after c = []; d = [], c and d are guaranteed to refer to two different, unique, newly created empty lists. (Note that c = d = [] assigns the same object to both c and d.)
So from the above statement we can infer that the strings, which are immutable types, may fail when checked with "is" and may succeed when checked with "is".
The same applies for int and tuple which are also immutable types.

is will compare the memory location. It is used for object-level comparison.
== will compare the variables in the program. It is used for checking at a value level.
is checks for address level equivalence
== checks for value level equivalence

is is identity testing and == is equality testing (see the Python documentation).
In most cases, if a is b, then a == b. But there are exceptions, for example:
>>> nan = float('nan')
>>> nan is nan
True
>>> nan == nan
False
So, you can only use is for identity tests, never equality tests.

The basic concept, we have to be clear, while approaching this question, is to understand the difference between is and ==.
"is" is will compare the memory location. if id(a)==id(b), then a is b returns true else it returns false.
So, we can say that is is used for comparing memory locations. Whereas,
== is used for equality testing which means that it just compares only the resultant values. The below shown code may acts as an example to the above given theory.
Code
In the case of string literals (strings without getting assigned to variables), the memory address will be same as shown in the picture. so, id(a)==id(b). The remaining of this is self-explanatory.

Object reference in python [duplicate]

Two string variables are set to the same value. s1 == s2 always returns True, but s1 is s2 sometimes returns False.
If I open my Python interpreter and do the same is comparison, it succeeds:
>>> s1 = 'text'
>>> s2 = 'text'
>>> s1 is s2
True
Why is this?

is is identity testing, and == is equality testing. What happens in your code would be emulated in the interpreter like this:
>>> a = 'pub'
>>> b = ''.join(['p', 'u', 'b'])
>>> a == b
True
>>> a is b
False
So, no wonder they're not the same, right?
In other words: a is b is the equivalent of id(a) == id(b)

Other answers here are correct: is is used for identity comparison, while == is used for equality comparison. Since what you care about is equality (the two strings should contain the same characters), in this case the is operator is simply wrong and you should be using == instead.
The reason is works interactively is that (most) string literals are interned by default. From Wikipedia:
Interned strings speed up string
comparisons, which are sometimes a
performance bottleneck in applications
(such as compilers and dynamic
programming language runtimes) that
rely heavily on hash tables with
string keys. Without interning,
checking that two different strings
are equal involves examining every
character of both strings. This is
slow for several reasons: it is
inherently O(n) in the length of the
strings; it typically requires reads
from several regions of memory, which
take time; and the reads fills up the
processor cache, meaning there is less
cache available for other needs. With
interned strings, a simple object
identity test suffices after the
original intern operation; this is
typically implemented as a pointer
equality test, normally just a single
machine instruction with no memory
reference at all.
So, when you have two string literals (words that are literally typed into your program source code, surrounded by quotation marks) in your program that have the same value, the Python compiler will automatically intern the strings, making them both stored at the same memory location. (Note that this doesn't always happen, and the rules for when this happens are quite convoluted, so please don't rely on this behavior in production code!)
Since in your interactive session both strings are actually stored in the same memory location, they have the same identity, so the is operator works as expected. But if you construct a string by some other method (even if that string contains exactly the same characters), then the string may be equal, but it is not the same string -- that is, it has a different identity, because it is stored in a different place in memory.

The is keyword is a test for object identity while == is a value comparison.
If you use is, the result will be true if and only if the object is the same object. However, == will be true any time the values of the object are the same.

One last thing to note is you may use the sys.intern function to ensure that you're getting a reference to the same string:
>>> from sys import intern
>>> a = intern('a')
>>> a2 = intern('a')
>>> a is a2
True
As pointed out in previous answers, you should not be using is to determine equality of strings. But this may be helpful to know if you have some kind of weird requirement to use is.
Note that the intern function used to be a built-in on Python 2, but it was moved to the sys module in Python 3.

is is identity testing and == is equality testing. This means is is a way to check whether two things are the same things, or just equivalent.
Say you've got a simple person object. If it is named 'Jack' and is '23' years old, it's equivalent to another 23-year-old Jack, but it's not the same person.
class Person(object):
def __init__(self, name, age):
self.name = name
self.age = age
def __eq__(self, other):
return self.name == other.name and self.age == other.age
jack1 = Person('Jack', 23)
jack2 = Person('Jack', 23)
jack1 == jack2 # True
jack1 is jack2 # False
They're the same age, but they're not the same instance of person. A string might be equivalent to another, but it's not the same object.

This is a side note, but in idiomatic Python, you will often see things like:
if x is None:
# Some clauses
This is safe, because there is guaranteed to be one instance of the Null Object (i.e., None).

If you're not sure what you're doing, use the '=='.
If you have a little more knowledge about it you can use 'is' for known objects like 'None'.
Otherwise, you'll end up wondering why things doesn't work and why this happens:
>>> a = 1
>>> b = 1
>>> b is a
True
>>> a = 6000
>>> b = 6000
>>> b is a
False
I'm not even sure if some things are guaranteed to stay the same between different Python versions/implementations.

From my limited experience with Python, is is used to compare two objects to see if they are the same object as opposed to two different objects with the same value. == is used to determine if the values are identical.
Here is a good example:
>>> s1 = u'public'
>>> s2 = 'public'
>>> s1 is s2
False
>>> s1 == s2
True
s1 is a Unicode string, and s2 is a normal string. They are not the same type, but they are the same value.

I think it has to do with the fact that, when the 'is' comparison evaluates to false, two distinct objects are used. If it evaluates to true, that means internally it's using the same exact object and not creating a new one, possibly because you created them within a fraction of 2 or so seconds and because there isn't a large time gap in between it's optimized and uses the same object.
This is why you should be using the equality operator ==, not is, to compare the value of a string object.
>>> s = 'one'
>>> s2 = 'two'
>>> s is s2
False
>>> s2 = s2.replace('two', 'one')
>>> s2
'one'
>>> s2 is s
False
>>>
In this example, I made s2, which was a different string object previously equal to 'one' but it is not the same object as s, because the interpreter did not use the same object as I did not initially assign it to 'one', if I had it would have made them the same object.

The == operator tests value equivalence. The is operator tests object identity, and Python tests whether the two are really the same object (i.e., live at the same address in memory).
>>> a = 'banana'
>>> b = 'banana'
>>> a is b
True
In this example, Python only created one string object, and both a and b refers to it. The reason is that Python internally caches and reuses some strings as an optimization. There really is just a string 'banana' in memory, shared by a and b. To trigger the normal behavior, you need to use longer strings:
>>> a = 'a longer banana'
>>> b = 'a longer banana'
>>> a == b, a is b
(True, False)
When you create two lists, you get two objects:
>>> a = [1, 2, 3]
>>> b = [1, 2, 3]
>>> a is b
False
In this case we would say that the two lists are equivalent, because they have the same elements, but not identical, because they are not the same object. If two objects are identical, they are also equivalent, but if they are equivalent, they are not necessarily identical.
If a refers to an object and you assign b = a, then both variables refer to the same object:
>>> a = [1, 2, 3]
>>> b = a
>>> b is a
True
Reference: Think Python 2e by Allen B. Downey

I believe that this is known as "interned" strings. Python does this, so does Java, and so do C and C++ when compiling in optimized modes.
If you use two identical strings, instead of wasting memory by creating two string objects, all interned strings with the same contents point to the same memory.
This results in the Python "is" operator returning True because two strings with the same contents are pointing at the same string object. This will also happen in Java and in C.
This is only useful for memory savings though. You cannot rely on it to test for string equality, because the various interpreters and compilers and JIT engines cannot always do it.

Actually, the is operator checks for identity and == operator checks for equality.
From the language reference:
Types affect almost all aspects of object behavior. Even the importance of object identity is affected in some sense: for immutable types, operations that compute new values may actually return a reference to any existing object with the same type and value, while for mutable objects this is not allowed. E.g., after a = 1; b = 1, a and b may or may not refer to the same object with the value one, depending on the implementation, but after c = []; d = [], c and d are guaranteed to refer to two different, unique, newly created empty lists. (Note that c = d = [] assigns the same object to both c and d.)
So from the above statement we can infer that the strings, which are immutable types, may fail when checked with "is" and may succeed when checked with "is".
The same applies for int and tuple which are also immutable types.

is will compare the memory location. It is used for object-level comparison.
== will compare the variables in the program. It is used for checking at a value level.
is checks for address level equivalence
== checks for value level equivalence

is is identity testing and == is equality testing (see the Python documentation).
In most cases, if a is b, then a == b. But there are exceptions, for example:
>>> nan = float('nan')
>>> nan is nan
True
>>> nan == nan
False
So, you can only use is for identity tests, never equality tests.

The basic concept, we have to be clear, while approaching this question, is to understand the difference between is and ==.
"is" is will compare the memory location. if id(a)==id(b), then a is b returns true else it returns false.
So, we can say that is is used for comparing memory locations. Whereas,
== is used for equality testing which means that it just compares only the resultant values. The below shown code may acts as an example to the above given theory.
Code
In the case of string literals (strings without getting assigned to variables), the memory address will be same as shown in the picture. so, id(a)==id(b). The remaining of this is self-explanatory.