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Python if-statements when the condition variable is boolean [duplicate]

Apologies if this has been asked before, but I have searched in vain for an answer to my exact question. Basically, with Python 2.7, I have a program running a series of geoprocessing tools, depended on what is reqested via a series of True/False variables that the user adjusts in the script e.g.
x = True
if x:
run function
However, I have now discovered that x does not need to be literally "True" for the function to run. For example:
In: x = True
if x:
print True
Out: True
In: x = 123
if x:
print True
Out: True
In: x = 'False'
if x:
print True
Out: True
In: x = False
if x:
print True
Out:
So any value other than False appears to evaluate to True, which would not be the case for if x == True or if x is True. Seeing as PEP 8 strongly recommends only using the if x: variant, can anybody explain why this behaviour occurs? It seems that if x: is more a test for "if x is not False" or "if x exists". With that in mind, I believe I should be using if x is True: in this case, despite what PEP 8 has to say.

The following values in Python are false in the context of if and other logical contexts:
False
None
numeric values equal to 0, such as 0, 0.0, -0.0
empty strings: '' and u''
empty containers (such as lists, tuples and dictionaries)
anything that implements __bool__ (in Python3) to return False, or __nonzero__ (in Python2) to return False or 0.
anything that doesn't implement __bool__ (in Python3) or __nonzero__ (in Python2), but does implement __len__ to return a value equal to 0
An object is considered "false" if any of those applies, and "true" otherwise, regardless of whether it's actually equal to or identical with False or True
Now, if you've arranged that x is necessarily one of the objects True or False, then you can safely write if x. If you've arranged that the "trueness" of x indicates whether or not to perform the operation, regardless of type, then you can safely write if x. Where you can write that you should prefer to do so, since it's cleaner to read.
Normally, if it is allowed for x to take the value True then you're in one of those two cases, and so you would not write if x is True. The important thing is to correctly document the meaning of x, so that it reflects the test used in the code.
Python programmers are expected to know what's considered true, so if you just document, "runs the function if x is true", then that expresses what your original code does. Documenting it, "runs the function if x is True" would have a different meaning, and is less commonly used precisely because of the style rule in PEP8 that says to test for trueness rather than the specific value True.
However, if you wanted the code to behave differently in the case where x is an empty container from the case where it is None, then you would write something like if x is not None.

I'd like to add a short example where those 3 tests differ:
def test(x):
print(x, ":", bool(x), x == True, x is True)
test("something")
test(1)
test(True)
The output (pretty formatted):
# "something" : True False False
# 1 : True True False
# True : True True True

x = 'False'
x = 123
Are both True
Other truth values.
The document explains other values.
As far as the PEP8 reason, its far more semantic to read if this_file_is_green

Other falsey values include 0, '', []. You should just use the if x: version.

It goes without saying that you should write code that does what you need. But in most cases, you simply don't need to say == True or is True, because you don't need to distinguish True from other "truthy" values. So it's recommended to leave that out for simplicity.
The case where you definitely should use == True or is True is when you do need to distinguish True from other truthy values.
In your example, do you care about the difference between True and 123? That would tell you which way to code it.
One thing about coding == True or is True: it will raise a minor red flag when other developers read your code. They won't think it's wrong, they will just wonder why it's there and will want to know why it's important to treat True differently from other truthy values in this particular case.
In other words, if you don't need it, it's best not to use it.

The ability to say
if x:
...
is considered a feature. You can also specify when the test should be considered to pass or not for user defined classes (just define the method __nonzero__ in Python 2.x or __bool__ in Python 3).
For example for strings and containers like lists, dictionaries or sets the test if x ... means "if x is not empty".
Note that the rationale is not that this allows less code to write, but that resulting code is easier to read and to understand.
If you like instead to write if x is True ... have you considered to go farther down that path to if (x is True) is True ... or if ((x is True) is True) is True ... ? :-)

In Python 2.7, if a: and if a==True are not giving the same output for values different to 1. Here are some snippets of code to demonstrate the different behaviors:
with a=1
a=1
if a==True:
print (a,"True")
else:
print (a,"Not True")
output> (1,True)
a=1
if a:
print (a,"True")
else:
print (a,"Not True")
output> (1, True)
with a=2
a=2
if a:
print (a,"True")
else:
print (a,"Not True")
output> (2, True)
a=2
if a==True:
print (a,"True")
else:
print (a,"Not True")
output> (2, Not True)

if you use if x ,it means it has to evaluate x for its truth value.But when you use x ==True or x is True.It means checking whether type(x)==bool and whether x is True.
attention : x is True is no equal to bool(x)==True
when you use x is True , you are checking the id of x and True.

why is id(1) is id(1) returning False?

I know the is operator compares the id of the two, not the value
However when you evaluate id(1) and id(1), the object (which is an int) is equal to one another.
So why is it returning false?
id(1) # 27533656
id(1) is id(1) # False
type(id(1)) # int
so then, technically, deduce it to
id(1) is id(1)
27533656 is 27533656 # has to be True!

is checks for object identity whereas == checks object equality. The difference can be subtle, but is most easy to see when dealing with mutable objects like lists:
x = [1, 2, 3]
y = x[:] # copy, not the same list
x is y # False
x == y # True
If I add something to x, y won't see it:
x.append(4)
x == y # now False since they have diverged.
But, if I alias x and append to it, the alias will also see the changes:
z = x # alias `z` to `x`
x.append(5)
x == z # True
And the reason for this is because z is x.
z is x # True
In this case, the id of 1 is consistent because CPython1 caches small integers for efficiency (so the object literal 1 is always the same object -- and therefore the same Id). However, the return value of id can actually be a different instances of int that have equivalent values.
1This is a well known implementation detail for CPython. Other implementations (and future versions) are free behave differently so do not rely on this behaviour.

You're slightly misinterpreting what the is syntax does.
What you are doing with that bit of code is roughly:
id(id(1)) == id(id(1))
Because the value returned by the id function is not static and not guaranteed to be the same identity, they will most likely be different identities.
As proof, when I put those into a print statement I get:
print id(id(1)) # 4327028
print id(id(1)) # 4326968
Which are obviously not the same.
If you were to change the code to:
if 1 is 1: # basically in this case id(1) == id(1)
print "success"
You would find that the values are indeed the same id.
As noted in the comments. The x is x and id(x) == id(x) methods of determining equivalence are not always the same for all literal types.
In the specific case of this question they may be treated as equal.

all builtin function of empty list

Can anybody explain why in python builtin buinction all return True in this case all([])?
In [33]: all([])
Out[33]: True
In [34]: all([0])
Out[34]: False
In [35]: __builtins__.all([])
Out[35]: True

I'm not convinced that any of the other answers have really address the question of why this should be the case.
The definition for Python's all() comes from boolean logic. If for example we say that "all swans are white" then a single black swan disproves the statement. However, if we say that "all unicorns are pink" logicians would take that as a true statement simply because there are no non-pink unicorns. Or in other words "all " is vacuously true.
Practically it gives us a useful invariant. If all(A) and all(B) are both true then the combination of all(A + B) is also true. If all({}) was false we should have a less useful situation because combining two expressions one of which is false suddenly gives an unexpected true result.
So Python takes all([]) == True from boolean logic, and for consistency with other languages with a similar construct.
Taking that back into Python, in many cases the vacuous truth makes algorithms simpler. For example, if we have a tree and want to validate all of the nodes we might say a node is valid if it meets some conditions and all of its children are valid. With the alternative definition of all() this becomes more complex as we have to say it is valid if it meets the conditions and either has no children or all its children are valid.
class Node:
def isValid(self):
return some_condition(self) and all(child.isValid for child in self.children)

From the docs:
Return True if all elements of the iterable are true (or if the iterable is empty).
So, roughly, it's simply defined this way.
You can get around that by using
list = []
if list and all(list):
pass

As the docs say, all is equivalent to:
def all(iterable):
for element in iterable:
if not element:
return False
return True
For an empty iterable the loop body is never executed, so True is immediately returned.

Another explanation for this is that all and any are generalisations of the binary operators and and or for arbitrarily long numbers of parameters. Thus, all and any can be defined as:
def all(xs):
return reduce(lambda x,y: x and y, xs, True)
def any(xs):
return reduce(lambda x,y: x or y, xs, False)
The True and False parameters show that all([]) == True and any([]) == False.

Any expression with all can be rewritten by any and vice versa:
not all(iterable)
# is the same as:
any(not x for x in iterable)
and symmetrically
not any(iterable)
# is the same as:
all(not x for x in iterable)
These rules require that all([]) == True.
The function all is very useful for readable asserts:
assert all(required_condition(x) for x in some_results_being_verified)
(It is not so bad if a task has no results, but something is very broken if any result is incorrect.)

Python - comparing long/integer values with == and is [duplicate]

This question already has answers here:
Closed 12 years ago.
Possible Duplicate:
Python “is” operator behaves unexpectedly with integers
Ran into something odd last night where doing
if max_urls is 0:
max_urls = 10
would always return false... even when max_urls was 0.... it was getting assigned from the database. When I did a
print type(max_urls)
would return
<type 'long'> 0
which seemed right but it would always return false.
If I changed it to
if max_urls == 0:
max_urls = 10
then finally it would return true when it was 0. Why the difference between == and is?

== is a value comparison, is is an object identity (memory location) comparison. You will often see that comparisons like max_urls is 0 will give the intended result because small values are usually cached in Python, but you always want to be using == instead of is when checking equality because this behavior cannot be relied upon.
Here is a brief example illustrating this:
>>> a = 0
>>> (a == 0, a is 0)
(True, True)
>>> a = 1000
>>> (a == 1000, a is 1000)
(True, False)

The is operator checks that two references point to the same object. You are testing if long(0) is the same object as int(0), and the answer is no. This will be crystal clear if you print their object ids:
>>> max_urls = long(0)
>>> id(max_urls)
335952
>>> id(0)
8402324
== on the other hand checks that two values are equivalent, even if they are not the exact same object. For instance:
>>> a = 777
>>> b = 777
>>> a is b
False
>>> a == b
True
>>> id(a)
8404568
>>> id(b)
8404640
Note: It is important that I used 777 and not a smaller number like 1 or 2. Quoting from the Python manual:
The current implementation keeps an array of integer objects for all integers between -5 and 256, when you create an int in that range you actually just get back a reference to the existing object.

Is there a difference between "==" and "is"?

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.