Occasionally in Python (and in programming in general), you’ll need an object which can be uniquely identified. Sometimes this unique object represents a stop value or a skip value and sometimes it’s an initial value. But in each of these cases you want your object to stand out from the other objects you’re working with.

When you need a unique value (a sentinel value maybe) None is often the value to reach for. But sometimes None isn’t enough: sometimes None is ambiguous.

In this article we’ll talk about when None isn’t enough, I’ll show you how I create unique values when None doesn’t cut it, and we’ll see a few different uses for this technique.

Initial values and default values

Let’s re-implement a version of Python’s built-in min function.

1 2 3 4 5 6 7 8 9 10 11 12 def min ( iterable , default = None ): """Imperfect re-implementation of Python's built-in min function.""" minimum = None for item in iterable : if minimum is None or item < minimum : minimum = item if minimum is not None : return minimum elif default is not None : return default else : raise ValueError ( "Empty iterable" )

This min function, like the built-in one, returns the minimum value in the given iterable or raises an exception when an empty iterable is given unless a default value is specified (in which case the default is returned).

1 2 3 4 5 6 7 8 9 10 11 12 13 >>> min ([ 4 , 3 , 8 , 7 ]) 3 >>> min ([ 9 ]) 9 >>> min ([]) Traceback ( most recent call last ): File "<stdin>" , line 1 , in < module > File "<stdin>" , line 12 , in min ValueError : Empty iterable >>> min ([], default = 9 ) 9 >>> min ([ 4 , 3 , 8 , 7 ], default = 9 ) 3

This behavior is somewhat similar to the built-in min function, except our code is buggy!

There are two bugs here.

First, an iterable containing a single None value will be treated as if it was an empty iterable:

1 2 3 4 5 6 7 >>> min ([ None ], default = 0 ) 0 >>> min ([ None ]) Traceback ( most recent call last ): File "<stdin>" , line 1 , in < module > File "<stdin>" , line 8 , in min ValueError : Empty iterable

Second, if we specify our default value as None this min function won’t accept it:

1 2 3 4 5 6 7 >>> min ([], default = '' ) '' >>> min ([], default = None ) Traceback ( most recent call last ): File "<stdin>" , line 1 , in < module > File "<stdin>" , line 12 , in min ValueError : Empty iterable

Why is this happening?

It’s all about None .

Why is None a problem?

The first bug in our code is related to the initial value for minimum and the second is related to the default value for our default argument. In both cases, we’re using None to represent an unspecified or un-initialized value.

1 2 3 4 5 6 7 8 9 10 11 12 def min ( iterable , default = None ): """Imperfect re-implementation of Python's built-in min function.""" minimum = None for item in iterable : if minimum is None or item < minimum : minimum = item if minimum is not None : return minimum elif default is not None : return default else : raise ValueError ( "Empty iterable" )

Using None is a problem in both cases because None is both a valid value for default and a valid value in our iterable.

Python’s None value is useful for representing emptiness, but it isn’t magical, at least not any more magical than any other valid value.

If we need a truly unique value for our default state, we need to invent our own.

When None isn’t a valid input for your function, it’s perfectly fine to use it to represent a unique default or initial state. But None is often valid data, which means None is sometimes a poor choice for a unique initial state.

We’ll fix both of our bugs by using object() : a somewhat common convention for creating a truly unique value in Python.

First we’ll set minimum to a unique object:

1 2 3 4 5 6 7 8 9 10 11 12 13 def min ( iterable , default = None ): """Imperfect re-implementation of Python's built-in min function.""" initial = object () minimum = initial for item in iterable : if minimum is initial or item < minimum : minimum = item if minimum is not initial : return minimum elif default is not None : return default else : raise ValueError ( "Empty iterable" )

That initial variable holds our unique value so we can check for its presence later.

This fixes the first bug:

1 2 3 4 5 6 7 >>> min ([ None ], default = 0 ) >>> min ([ None ]) >>> min ([]) Traceback ( most recent call last ): File "<stdin>" , line 1 , in < module > File "<stdin>" , line 13 , in min ValueError : Empty iterable

But not the second.

To fix the second bug we need to use a different default value for our default argument (other than None ).

To do this, we’ll make a global “constant” (by convention) variable, INITIAL , outside our function:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 INITIAL = object () def min ( iterable , default = INITIAL ): """Imperfect re-implementation of Python's built-in min function.""" minimum = INITIAL for item in iterable : if minimum is INITIAL or item < minimum : minimum = item if minimum is not INITIAL : return minimum elif default is not INITIAL : return default else : raise ValueError ( "Empty iterable" )

Now our code works exactly how we’d hope it would:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 >>> min ([ None ], default = 0 ) >>> min ([ None ]) >>> min ([], default = None ) >>> min ([], default = '' ) '' >>> min ([ 4 , 3 , 7 , 8 ]) 3 >>> min ([ 4 , 3 , 7 , 8 ], default = 0 ) 3 >>> min ([]) Traceback ( most recent call last ): File "<stdin>" , line 1 , in < module > File "<stdin>" , line 12 , in min ValueError : Empty iterable

That’s lovely… but what is this magical object() thing? Why does it work, how does it work, and when should we use it?

What is object() ?

Every class in Python has a base class of object (in Python 3 that is… things were a bit weirder in Python 2).

So object is a class:

1 2 3 4 >>> object < class ' object '> >>> type ( object ) < class ' type '>

When we call object we’re creating an “instance” of the object class, just as calling any other class (when given the correct arguments) will create instances of them:

1 2 3 4 5 6 >>> set () set () >>> bytearray () bytearray ( b '' ) >>> frozenset () frozenset ()

So we’re creating an instance of object . But… why?

Well, an instance of object shouldn’t be seen as equal to any other object:

1 2 3 4 5 6 7 8 9 10 >>> x = object () >>> y = object () >>> x == y False >>> x == 4 False >>> x == None False >>> x == [] False

Except itself:

1 2 3 4 >>> x = object () >>> z = x >>> x == z True

Python’s None is similar, except that anyone can get access to this unique None object anywhere in their code by just typing None .

1 2 3 4 5 6 7 8 >>> x = None >>> y = None >>> x == y True >>> x = object () >>> y = object () >>> x == y False

We needed a placeholder value in our code. None is a lovely placeholder as long as we don’t need to worry about distinguishing between our None and their None .

If None is valid data, it’s no longer just a placeholder. At that point, we need to start reaching for object() instead.

Equality vs identity

I noted that object() isn’t equal to anything else. But we weren’t actually checking for equality (using == or != ) in our function:

Instead of == and != , we used is and is not .

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 INITIAL = object () def min ( iterable , default = INITIAL ): """Imperfect re-implementation of Python's built-in min function.""" minimum = INITIAL for item in iterable : if minimum is INITIAL or item < minimum : minimum = item if minimum is not INITIAL : return minimum elif default is not INITIAL : return default else : raise ValueError ( "Empty iterable" )

While == and != are equality operators, is and is not are identity operators.

Python’s is operator asks about the identity of an object: are the two objects on either side of the is operator actually the same exact object.

We’re not just asking are they equal, but are they stored in the same place in memory and in fact refer to the same exact object.

Two of the variables below ( x and z ) point to the same object:

1 2 3 >>> x = object () >>> y = object () >>> z = x

So while y has a unique ID in memory, x and z do not:

1 2 3 4 5 6 >>> id ( x ) 140079600030400 >>> id ( y ) 140079561403808 >>> id ( z ) 140079600030400

Which means x is identical to z :

1 2 3 4 >>> x is y False >>> x is z True

By default, Python’s == operator delegates to is . Meaning unless two variables point to the exact some object in memory, == will return False :

1 2 3 4 5 6 7 8 9 >>> x = object () >>> y = object () >>> z = x >>> x == x True >>> x == y False >>> x == z True

This is true by default… but many objects in Python overload the == operator to do much more useful things when we ask about equality.

1 2 3 4 5 6 7 8 >>> 0 == 0.0 True >>> [ 1 , 2 , 3 ] == [ 1 , 2 , 3 ] True >>> ( 1 , 2 ) == ( 1 , 3 ) False >>> {} == {} True

Each object can customize the behavior of == to answer whatever question they’d like.

Which means someone could make a class like this:

1 2 3 4 >>> class AlwaysEqual : ... def __eq__ ( self , other ): ... return True ...

And suddenly our assumption about == with object() (or any other value) will fail us:

1 2 3 4 5 6 >>> x = object () >>> y = AlwaysEqual () >>> x is y False >>> x == y True

Use identity to compare unique objects

The is operator, unlike == , is not overloadable. Unlike with == , there’s no way to control or change what happens when you say x is y .

There’s a __eq__ method, but there’s no such thing as a __is__ method. Which means the is operator will never lie to you: it will always tell you whether two objects are one in the same.

If we use is instead of == , we could actually use any unique object to represent our unique INITIAL value.

Even an empty list:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 INITIAL = [] def min ( iterable , default = INITIAL ): """Imperfect re-implementation of Python's built-in min function.""" minimum = INITIAL for item in iterable : if minimum is INITIAL or item < minimum : minimum = item if minimum is not INITIAL : return minimum elif default is not INITIAL : return default else : raise ValueError ( "Empty iterable" )

An empty list might seem problematic in the same way as None was: but they’re actually quite different.

We don’t have any of the same issues as we did with None before:

1 2 3 4 5 6 >>> min ([[]], default = 0 ) [] >>> min ([[]]) [] >>> min ([], default = []) []

The reason is that None is a singleton value. That means that whenever you say None in your Python code, you’re referencing the exact same None object every time.

1 2 3 4 5 6 >>> x = None >>> y = None >>> x is y True >>> id ( x ), id ( y ) ( 94548887510464 , 94548887510464 )

Whereas every empty list we make creates a brand new list object:

1 2 3 4 5 6 >>> x = [] >>> y = [] >>> x is y False >>> id ( x ), id ( y ) ( 140079561624776 , 140079598927432 )

So while two independent empty lists may be equal, they aren’t the same object:

1 2 3 4 5 6 >>> x = [] >>> y = [] >>> x == y True >>> x is y False

The objects that those x and y variables point to have the same value but are not actually the same object.

None is a placeholder value

Python’s None is lovely. None is a universal placeholder value. Need a placeholder? Great! Python has a great placeholder value and it’s called None !

There are lots of places where Python itself actually uses None as a placeholder value also.

If you pass no arguments to the string split method, that’s the same as passing a separator value of None :

1 2 3 4 5 >>> s = "hello world" >>> s . split () [ 'hello' , 'world' ] >>> s . split ( None ) [ 'hello' , 'world' ]

If you pass in a key function of None to the sorted builtin, that’s the same as passing in no key function at all:

1 2 3 4 >>> sorted ( s , key = None ) [ ' ' , 'd' , 'e' , 'h' , 'l' , 'l' , 'l' , 'o' , 'o' , 'r' , 'w' ] >>> sorted ( s ) [ ' ' , 'd' , 'e' , 'h' , 'l' , 'l' , 'l' , 'o' , 'o' , 'r' , 'w' ]

Python loves using None as a placeholder because it’s often a pretty great placeholder value.

The issue with None only appears if someone else could reasonably be using None as a non-placeholder input to our function. This is often the case when the caller of a function has a placeholder values (often None ) in their inputs and the author of that function (that’s us) needs a separate unique placeholder.

Using None to represent two different things at once is like having two identical-looking bookmarks in the same book: it’s confusing!

Creating unique non-None placeholders: why object() ?

When we made that INITIAL value before, we were sort of inventing our own None -like object: an object that we could uniquely reference by using the is operator.

That INITIAL object we made should be completely unique: it shouldn’t ever be seen in any arbitrary input that may be given to our function (unless someone made the strange decision to import INITIAL and reference it specifically).

Why object() though? After all we could have used any unique object by creating an instance of pretty much any class:

1 2 3 4 5 >>> INITIAL = [] >>> INITIAL == [] True >>> INITIAL is [] False

Though it might have been even more clear to create our own class just for this purpose:

1 2 3 4 class DummyClass : """Class that just creates unique objects.""" INITIAL = DummyClass ()

But I’d argue that object() is the “right” thing to use here.

Everyone knows what [] means, but object() is mysterious, which is actually the reason I think it’s a good choice in this case.

When we see an empty list we expect that list to be used as a list and when we see a class instance, we expect that class to do something. But we don’t actually want this object to do anything: we only care about the uniqueness of this new object.

We could have done this:

1 >>> INITIAL = [ 'completely unique value' ]

But I find using object() less confusing than this because it’s clear: readers won’t have a chance to be confused by the listy-ness of a list.

1 >>> INITIAL = object () # completely unique value

Also if a confused developer Googles “what is object() in Python?” they might end up with some sort of explanation.

Other cases for non-None placeholders

There’s a word I’ve been avoiding using up to this point. I’ve only been avoiding it because I think I typically misuse it (or rather overuse it). The word is sentinel value.

I suspect I overuse this word because I use it to mean any unique placeholder value, such as the INITIAL object we made before. But most definitions I’ve seen use “sentinel value” to specifically mean a value which indicates the end of a list, a loop, or an algorithm.

Sentinel values are a thing that, when seen, indicate that something has finished. I think of this as a stop value: when you see a sentinel value it’s a signal that the loop or algorithm that you’re in should terminate.

Before we weren’t using a stop value so much as an initial value.

Here’s an example of a stop value; a true sentinel value:

1 2 3 4 5 6 7 8 9 10 from itertools import zip_longest SENTINEL = object () def strict_zip ( * iterables ): """Variation of ``zip`` which requires equal-length iterables.""" for values in zip_longest ( * iterables , fillvalue = SENTINEL ): if SENTINEL in values : raise ValueError ( "Given iterables must have the same length." ) yield values

We’re using the unique SENTINEL value above to signal that we need to stop looping and raise an exception. The presence of this value indicates that one of our iterables was a different length than the others and we need to handle this error case.

Rely on identity checks for unique values

Note that we’re implicitly relying on == above because we’re saying if SENTINEL in values which actually loops over values looking for a value that is equal to SENTINEL .

If we wanted to be more strict (and possibly more efficient) we could rely on is , but we’d need to do some looping ourselves. Fortunately Python’s any function and a generator expression would make that a bit easier:

1 2 3 4 5 6 7 8 9 10 from itertools import zip_longest SENTINEL = object () def strict_zip ( * iterables ): """Variation of ``zip`` which requires equal-length iterables.""" for values in zip_longest ( * iterables , fillvalue = SENTINEL ): if any ( v is SENTINEL for v in values ): raise ValueError ( "Given iterables must have the same length." ) yield values

I’m fine with either of these functions. The first is a bit more readable even though this one is arguably a bit more correct.

Identity checks are often faster than equality checks ( == has to call the __eq__ method, but is does a straight memory ID check). But identity checks are also a bit more correct: if it’s uniqueness we care about, a unique memory location is the ultimate uniqueness check.

When writing code that uses a unique object, it’s wise to rely on identity rather than equality if you can.

This is what is was made for

If we care about equality (the value of an object) we use == , if we care about identity (the memory location) we use is .

If you search my Python code for is you’ll pretty much only find the following things:

x is None (this is the most common thing you’ll see) x is True or x is False (sometimes my tests get picky about True vs truthiness) iter(x) is x (iterators are a different Python rabbit hole) x is some_unique_object

Those first two are checking for a singleton value (as recommended by PEP 8). The third one is checking if we’ve seen the same object twice (an iterator in this case). And the fourth one is checking for the presence of these unique values we’ve been discussing.

The is operator checks whether two objects are exactly the same object in memory. You never want to use the is operator except for true identity checks: singletons (like None , True , and False ), checking for the same object again, and checking for our own unique values (sentinels, as I usually call them).

So when would we use object() ?

Oftentimes None is both the easy answer and the right answer for a unique placeholder value in Python, but sometimes you just need to invent your own unique placeholder value. In those cases object() is a great tool to have in your Python toolbox.

When would we actually use object() for a uniqueness check in our own code?

I can think of a few cases:

Unique initial values: a starting value that should be distinguished from values seen later ( default and initial in our min function) Unique stop values: a value whose presence tells us to stop looping/processing (a true sentinel value, as in strict_zip ) Unique skip values: a value whose presence should be treated as an empty value to be skipped over (we didn’t see this, but it comes up with utilities like itertools.zip_longest sometimes)

I hope this meandering through unique values has given you something (some non- None things) to think about.

May your None values be unambiguous and your identity checks be truly unique.

Practice what you just learned

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If you sign up to Python Morsels (my Python skill-building service) using the form below, I’ll immediately send you a Python exercise where it makes sense to use object() .