Learn the ins and outs of comparison operator chaining, and especially the cases you should avoid.

A Python code snippet showing comparison operator chaining.

(If you are new here and have no idea what a Pydon't is, you may want to read the Pydon't Manifesto.)


In this Pydon't we will go over the chaining of comparison operators:

  • how they work;
  • useful usages; and
  • weird cases to avoid.

Chaining of comparison operators

One of the things I enjoy about Python is that some of its features make so much sense that you don't even notice that you are using a feature until someone points out that such code wouldn't work in other languages. One such example is comparison chaining! Look at this snippet of code and tell me if it doesn't look natural:

>>> a = 1
>>> b = 2
>>> c = 3
>>> if a < b < c:
...     print("Increasing seq.")
Increasing seq.

When Python sees two comparison operators in a row, like in a < b < c, it behaves as if you had written something like a < b and b < c, except that b only gets evaluated once (which is relevant if b is an expression like a function call).

In my opinion, this features makes a lot of sense and does not look surprising. Instead, now I feel kind of sad that most languages do not have support for this behaviour.

Another example usage is for when you want to make sure that three values are all the same:

>>> a = b = 1
>>> c = 2
>>> if a == b == c:
...     print("all same")
... else:
...     print("some are diff")
some are diff
>>> c = 1
>>> if a == b == c:
...     print("all same")
... else:
...     print("some are diff")
all same

Did you know that you can actually chain an arbitrary number of comparison operators? For example, a == b == c == d == e checks if all five variables are the same, while a < b < c < d < e checks if you have a strictly increasing sequence.


Even though this feature looks very sensible, there are a couple of pitfalls you have to look out for.

Non-transitive operators

We saw above that we can use a == b == c to check if a, b and c are all the same. How would you check if they are all different?

If you thought about a != b != c, then you just fell into the first pitfall!

Look at this code:

>>> a = c = 1
>>> b = 2
>>> if a != b != c:
...     print("a, b, and c all different:", a, b, c)
a, b, and c all different: 1 2 1

The problem here is that a != b != c is a != b and b != c, which checks that b is different from a and from c, but says nothing about how a and c relate.

From the mathematical point of view, != isn't transitive, i.e., knowing how a relates to b and knowing how b relates to c doesn't tell you how a relates to c. As for a transitive example, you can take the == equality operator. If a == b and b == c then it is also true that a == c.

Non-constant expressions or side-effects

Recall that in a chaining of comparisons, like a < b < c, the expression b in the middle is only evaluated once, whereas if you were to write the expanded expression, a < b and b < c, then b would get evaluated twice.

If b contains an expression with side-effects or if it is something that isn't constant, then the two expressions are not equivalent and you should think about what you are doing.

This snippet shows the difference in number of evaluations of the expression in the middle:

>>> def f():
...     print("hey")
...     return 3
>>> if 1 < f() < 5:
...     print("done")
>>> if 1 < f() and f() < 5:
...     print("done")

This snippet shows that an expression like 1 < f() < 0 can actually evaluate to True when it is unfolded:

>>> l = [-2, 2]
>>> def f():
...     global l
...     l = l[::-1]
...     return l[0]
>>> if 1 < f() and f() < 0:
...     print("ehh")

The syntax l[::-1] is a “slice” that reverses a list. I'll be writing about list slicing soon, so stay tuned for that!

Of course that 1 < f() < 0 should never be True, so this just shows that the chained comparison and the unfolded one aren't always equivalent.

Ugly chains

This feature looks really natural, but some particular cases aren't so great. This is a fairly subjective matter, but I personally don't love chains where the operators aren't "aligned", so chains like

  • a == b == c
  • a < b <= c
  • a <= b < c

look really good, but in my opinion chains like

  • a < b > c
  • a <= b > c
  • a < b >= c

don't look that good. One can argue, for example, that a < b > c reads nicely as “check if b is larger than both a and c”, but you could also write max(a, c) < b or b > max(a, c).

Now there's some other chains that are just confusing:

  • a < b is True
  • a == b in l
  • a in l is True

In Python, is, is not, in, and not in are comparison operators, so you can also chain them with the other operators. This creates weird situations like

>>> a = 3
>>> l = [3, 5]
>>> if a in l == True:
...     print("Yeah :D")
... else:
...     print("Hun!?")

Here is a breakdown of what is happening in the previous example:

  • a in l == True is equivalent to a in l and l == True;
  • a in l is True, but
  • l == True is False, so
  • a in l == True unfolds to True and False which is False.

The one who wrote a in l == True probably meant (a in l) == True, but that is also the same as a in l.

Examples in code

Inequality chain

Having a simple utility function that ensures that a given value is between two bounds becomes really simple, e.g.

def ensure_within(value, bounds):
    return bounds[0] <= value <= bounds[1]

or if you want to be a little bit more explicit, while also ensuring bounds is a vector with exactly two items (take a look at the Pydon't about deep unpacking), you can also write

def ensure_within(value, bounds):
    m, M = bounds
    return m <= value <= M

Equality chain

Straight from Python's enum module, we can find a helper function (that is not exposed to the user), that reads as follows:

def _is_dunder(name):
    """Returns True if a __dunder__ name, False otherwise."""
    return (len(name) > 4 and
            name[:2] == name[-2:] == '__' and
            name[2] != '_' and
            name[-3] != '_')

This function checks if a string is from a “dunder” method or not.

“Dunder” comes from “double underscore” and just refers to some Python methods that some classes have, and that allow them to interact nicely with many of Python's built-in features. These methods are called “dunder” because their names start and end with __. You have seen the __str__ and __repr__ dunder methods in the “str and repr” Pydon't and the __bool__ dunder method in the “Truthy, falsy, and bool” Pydon't. I will be writing about dunder methods in general in a later Pydon't, so feel free to subscribe to stay tuned.

The first thing the code does is check if the beginning and the ending of the string are the same and equal to "__":

>>> _is_dunder("__str__")
>>> _is_dunder("__bool__")
>>> _is_dunder("_dnd__") 
>>> _is_dunder("_______underscores__")


Here's the main takeaway of this article, for you, on a silver platter:

“Chaining comparison operators feels so natural, you don't even notice it is a feature. However, some chains might throw you off if you overlook them.”

This Pydon't showed you that:

  • you can chain comparisons, and do so arbitrarily many times;
  • chains with expressions that have side-effects or with non-deterministic outputs are not equivalent to the extended version; and
  • some chains using is or in can look really misleading.

If you liked this Pydon't be sure to leave a reaction below and share this with your friends and fellow Pythonistas.

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Online references last consulted on the 1st of March of 2021.

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