## Boolean short-circuiting | Pydon't đ

In this Pydon't we explore what Boolean short-circuiting for the and and or operators is, and how to use this functionality to write more expressive code.

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

# Introduction

In this Pydon't we will take a closer look at how and and or really work and at a couple of really neat things you can do because of the way they are defined. In particular, we will look at

• the fact that and and or return values from their operands, and not necessarily True or False;
• what âshort-circuitingâ is and how to make the best use of it;
• how short-circuiting in and and or extends to all and any; and
• some expressive use-cases of Boolean short-circuiting.

For this Pydon't, I will assume you are familiar with what âTruthyâ and âFalsyâ values are in Python. If you are not familiar with this concept, or if you would like just a quick reminder of how this works, go ahead and read the âTruthy, Falsy, and boolâ Pydon't.

# Return values of the and and or operators

If we take a look at the docs, here is how or is defined:

âx or y returns y if x is false, otherwise it returns x.â

Equivalently, but written with an if expression,

(x or y) == (y if not x else x)

This may not seem like it is worth spending a thought on, but already right at this point we can see something very interesting: even though we look at the truthy or falsy value of x, what we return are the values associated with x/y, and not a Boolean value.

For example, look at the program below and think about what it outputs:

if 3 or 5:
print("Yeah.")
else:
print("Nope.")

If you thought it should print âYeah.â, you are right! Notice how 3 or 5 was the condition of the if statement and it evaluated to True, which is why the statement under if got executed.

Now, look at the program below and think about what it outputs:

print(3 or 5)

What do you think it outputs? If you think the output should be True, you are wrong! The program above outputs 3:

>>> 3 or 5
3

Let's go back to something I just said:

âNotice how 3 or 5 was the condition of the if statement and it evaluated to True, which is why the statement under if got executed.â

The wording of this statement is wrong, but the error in it is fairly subtle. If you spotted it before I pointed it out, give yourself a pat in the back, you deserve it. So, what did I say wrong?

3 or 5 does not evaluate to True! It evaluates to 3, which is truthy and therefore tells the if to execute its statements. Returning True or a truthy value is something significantly different.

A similar thing happens with and. As per the docs, and can be defined as follows:

âx and y returns x if x is false, otherwise it returns y.â

We can also rewrite this as

(x and y) == (x if not x else y)

Take your time to explore this for a bit, just like we explored x or y above.

# Short-circuiting

You might be asking why this distinction is relevant. It is mostly relevant because of the following property: and and or only evaluate the right operand if the left operand is not enough to determine the result of the operation. This is what short-circuiting is: not evaluating the whole expression (stopping short of evaluating it) if we already have enough information to determine the final outcome.

This short-circuiting feature, together with the fact that the boolean operators and and or return the values of the operands and not necessarily a Boolean, means we can do some really neat things with them.

## or

### False or y

or evaluates to True if any of its operands is truthy. If the left operand to or is False (or falsy, for that matter) then the or operator has to look to its right operand in order to determine the final result.

Therefore, we know that an expression like

val = False or y

will have the value of y in it, and in an if statement or in a while loop, it will evaluate the body of the construct only if y is truthy:

>>> y = 5   # truthy value.
>>> if False or y:
...     print("Got in!")
... else:
...     print("Didn't get in...")
...
Got in!
>>> y = []  # falsy value.
>>> if False or y:
...     print("Got in 2!")
... else:
...     print("Didn't get in 2...")
...
Didn't get in 2...

Let this sit with you: if the left operand to or is False or falsy, then we need to look at the right operand to determine the value of the or.

### True or y

On the other hand, if the left operand to or is True, we do not need to take a look at y because we already know the final result is going to be True.

Let us create a simple function that returns its argument unchanged but that produces a side-effect of printing something to the screen:

def p(arg):
print(f"Inside p with arg={arg}")
return arg

Now we can use p to take a look at the things that Python evaluates when trying to determine the value of x or y:

>>> p(False) or p(3)
Inside p with arg=False
Inside p with arg=3
3
>>> p(True) or p(3)
Inside p with arg=True
True

Notice that, in the second example, p only did one print because it never reached the p(3).

### Short-circuiting of or expressions

Now we tie everything together. If the left operand to or is False or falsy, we know that or has to look at its right operand and will, therefore, return the value of its right operand after evaluating it. On the other hand, if the left operand is True or truthy, or will return the value of the left operand without even evaluating the right operand.

## and

We now do a similar survey, but for and.

### False and y

and gives True if both its operands are True. Therefore, if we have an expression like

val = False and y

do we need to know what y is in order to figure out what val is? No, we do not, because regardless of whether y is True or False, val is always False:

>>> False and True
False
>>> False and False
False

If we take the False and y expressions from this example and compare them with the if expression we wrote earlier, which was

(x and y) == (x if not x else y)

we see that, in this case, x was substituted by False, and, therefore, we have

(False and y) == (False if not False else y)

Now, the condition inside that if expression reads

not False

which we know evaluates to True, meaning that the if expression never returns y.

If we consider any left operand that can be False or falsy, we see that and will never look at the right operand:

>>> p([]) and True  # [] is falsy
Inside p with arg=[]
[]
>>> p(0) and 3242   # 0 is falsy
Inside p with arg=0
0
>>> p({}) and 242   # {} is falsy
Inside p with arg={}
{}
>>> p(0) and p(0)   # both are falsy, but only the left matters
Inside p with arg=0
0

### True and y

Now, I invite you to take a moment to work through the same reasoning, but with expressions of the form True and y. In doing so, you should figure out that the result of such an expression is always the value of y, because the left operand being True, or any other truthy value, doesn't give and enough information.

### Short-circuiting of and expressions

Now we tie everything together. If the left operand to and is False or falsy, we know the expression returns the value of the left operand regardless of the right operand, and therefore we do not even evaluate the right operand. On the other hand, if the left operand to and is True, then and will evaluate the right operand and return its value.

# Short-circuiting in plain English

Instead of memorising rules about what sides get evaluated when, just remember that both and and or will evaluate as many operands as needed to determine the overall Boolean result, and will then return the value of the last side that they evaluated.

As an immediate conclusion, the left operand is always evaluated, as you might imagine.

If you understand that, then it is just a matter of you knowing how and and or work from the Boolean perspective.

# all and any

The built-in functions all and any also short-circuit, as they are simple extensions of the behaviours provided by and and or, respectively.

all wants to make sure that all the values of its argument are truthy, so as soon as it finds a falsy value, it knows it's game over. That's why the docs say all is equivalent to the following code:

def all(it):
for elem in it:
if not elem:
return False
return True

Similarly, any is going to do its best to look for some value that is truthy. Therefore, as soon as it finds one, any knows it has achieved its purpose and does not need to evaluate the other values.

Can you write an implementation of any that is similar to the above implementation of all and that also short-circuits?

# Short-circuiting in chained comparisons

A previous Pydon't has shown you that comparison operators can be chained arbitrarily, and those are almost equivalent to a series of comparisons separated with and, except that the subexpressions are only evaluated once, to prevent wasting resources. Therefore, because we are also using an and in the background, chained comparisons can also short-circuit:

# 1 > 2 is False, so there's no need to look at p(2) < p(3)
>>> p(1) > p(2) < p(3)
Inside p with arg=1
Inside p with arg=2
False

# Examples in code

Now that we have taken a look at how all of these things work, we will see how to put them to good use in actual code.

## Short-circuit to save time

One of the most basic usages of short-circuiting is to save time. When you have a while loop or an if statement with multiple statements, you may want to include the faster expressions before the slower ones, as that might save you some time if the result of the first expression ends up short-circuiting.

### Conditionally creating a text file

Consider this example that should help me get my point across: imagine you are writing a function that creates a helper .txt file but only if it is a .txt file and if it does not exist yet.

With this preamble, your function needs to do two things:

• check the suffix of the file is .txt;
• check if the file exists in the filesystem.

What do you feel is faster? Checking if the file ends in .txt or looking for it in the whole filesystem? I would guess checking for the .txt ending is simpler, so that's the expression I would put first in the code:

import pathlib

def create_txt_file(filename):
path = pathlib.Path(filename)
if filename.suffix == ".txt" and not path.exists():
# Create the file but leave it empty.
with path.open():
pass

This means that, whenever filename does not respect the .txt format, the function can exist right away and doesn't even need to bother the operating system with asking if the file exists or not.

### Conditionally checking if a string matches a regular expression

Now let me show you a real example of an if statement that uses short-circuiting in this way, saving some time. For this, let us take a look at a function from the base64 module, that we take from the Python Standard Library:

# From Lib/base64.py in Python 3.9.2
def b64decode(s, altchars=None, validate=False):
"""Decode the Base64 encoded bytes-like object or ASCII string s.
[docstring cut for brevity]
"""
s = _bytes_from_decode_data(s)
if altchars is not None:
altchars = _bytes_from_decode_data(altchars)
assert len(altchars) == 2, repr(altchars)
s = s.translate(bytes.maketrans(altchars, b'+/'))
if validate and not re.fullmatch(b'[A-Za-z0-9+/]*={0,2}', s):   # <--
raise binascii.Error('Non-base64 digit found')
return binascii.a2b_base64(s)

This b64decode function takes a string (or a bytes-like object) that is assumed to be in base 64 and decodes it.

Here is a quick demo of that:

>>> import base64
>>> s = b"Base 64 encoding and decoding."
>>> enc = base64.b64encode(s)
>>> enc
b'QmFzZSA2NCBlbmNvZGluZyBhbmQgZGVjb2Rpbmcu'
>>> base64.b64decode(enc)
b'Base 64 encoding and decoding.'

Now, look at the if statement that I marked with a comment:

if validate and not re.fullmatch(b'[A-Za-z0-9+/]*={0,2}', s):
pass

validate is an argument to b64decode that tells the function if we should validate the string that we want to decode or not, and then the re.fullmatch() function call does that validation, ensuring that the string to decode only contains valid base 64 characters. In case we want to validate the string and the validation fails, we enter the if statement and raise an error.

Notice how we first check if the user wants to validate the string and only then we run the regular expression match. We would obtain the exact same result if we changed the order of the operands to and, but we would be spending much more time than needed.

To show that, let us try both cases! Let's build a string with 1001 characters, where only the last one is invalid. Let us compare how much time it takes to run the boolean expression with the regex validation before and after the Boolean validate.

import timeit

# Code that sets up the variables we need to evaluate the expression that we
# DO NOT want to be taken into account for the timing.
setup = """
import re
s = b"a"*1000 + b"*"
validate = False
"""

# with    short-circuiting: 0.01561140s on my machine.
print(timeit.timeit("validate and not re.fullmatch(b'[A-Za-z0-9+/]*={0,2}', s)", setup))
# without short-circuiting: 27.4744187s on my machine.
print(timeit.timeit("not re.fullmatch(b'[A-Za-z0-9+/]*={0,2}', s) and validate", setup))

Notice that short-circuiting speeds up these comparisons by a factor of ~1750.

Of course we could try longer or shorter strings, we could try strings that pass the validation and we could also try strings that fail the validation at an earlier stage, but this is just a small example that shows how short-circuiting can be helpful.

## Short-circuit to flatten if statements

Short-circuiting can, and should, be used to keep if statements as flat as possible.

### Conditional validation

A typical usage pattern is when we want to do some validation if certain conditions are met.

Keeping the previous b64decode example in mind, that previous if statement could've been written like so:

# Modified from Lib/base64.py in Python 3.9.2
def b64decode(s, altchars=None, validate=False):
"""Decode the Base64 encoded bytes-like object or ASCII string s.
[docstring cut for brevity]
"""
s = _bytes_from_decode_data(s)
if altchars is not None:
altchars = _bytes_from_decode_data(altchars)
assert len(altchars) == 2, repr(altchars)
s = s.translate(bytes.maketrans(altchars, b'+/'))
# Do we want to validate the string?
if validate:                                            # <--
# Is the string valid?
if not re.fullmatch(b'[A-Za-z0-9+/]*={0,2}', s):    # <--
raise binascii.Error('Non-base64 digit found')
return binascii.a2b_base64(s)

Now we took the actual validation and nested it, so that we have two separate checks: one tests if we need to do validation and the other one does the actual validation. What is the problem with this? From a fundamentalist's point of view, you are clearly going against the Zen of Python, that says

âFlat is better than nested.â

But from a practical point of view, you are also increasing the vertical space that your function takes up by having a ridiculous if statement hang there. What if you have multiple conditions that you need to check for? Will you have a nested if statement for each one of those?

This is exactly what short-circuiting is useful for! Only running the second part of a Boolean expression if it is relevant!

### Checking preconditions before expression

Another typical usage pattern shows up when you have something you need to check, for example you need to check if a variable names is a list containing strings or you need to check if a given argument term is smaller than zero. It may happen that, in that context, it is not a good idea to do those checks immediately:

• the variable names might not be a list or might be empty; or
• the argument term might be of a different type and, therefore, might be incomparable to zero.

Here is a concrete example of what I mean:

# From Lib/asynchat in Python 3.9.2
def set_terminator(self, term):
"""Set the input delimiter.

Can be a fixed string of any length, an integer, or None.
"""
if isinstance(term, str) and self.use_encoding:
term = bytes(term, self.encoding)
elif isinstance(term, int) and term < 0:
raise ValueError('the number of received bytes must be positive')
self.terminator = term

This is a helper function from within the asynchat module. We don't need to know what is happening outside of this function to understand the role that short-circuiting has in the elif statement. If the term variable is smaller than 0, then we want to raise a ValueError to complain, but the previous if statement shows that term might also be a string. If term is a string, then comparing it with 0 raises another ValueError, so what we do is start by checking a necessary precondition to term < 0: term < 0 only makes sense if term is an integer, so we start by evaluating isinstance(term, int) and only then running the comparison.

Let me show you another example from the enum module:

# From Lib/enum.py in Python 3.9.2
def _create_(cls, class_name, names, *, module=None, qualname=None, type=None, start=1):
"""
Convenience method to create a new Enum class.
"""
# [cut for brevity]

# special processing needed for names?
if isinstance(names, str):
names = names.replace(',', ' ').split()
if isinstance(names, (tuple, list)) and names and isinstance(names[0], str):
original_names, names = names, []
last_values = []
for count, name in enumerate(original_names):
value = first_enum._generate_next_value_(name, start, count, last_values[:])
last_values.append(value)
names.append((name, value))

# [cut for brevity]

The longer if statement contains three expressions separated by ands, and the first two expressions are there to make sure that the final one,

isinstance(names[0], str)

makes sense. You can read along the statement and thing about what it means if execution reaches that point:

if isinstance(names, (tuple, list)) and names and isinstance(names[0], str):
#^ lets start checking this if statement.

if isinstance(names, (tuple, list)) and names and isinstance(names[0], str):
#                                   ^
# we only need to take a look at the right-hand side of this and if names
# is either a tuple or a list.

if isinstance(names, (tuple, list)) and names and isinstance(names[0], str):
#                                             ^
# at this point, I've checked if names is a list or a tuple and I have
# checked if it is truthy or falsy (i.e., checked if it is empty or not).
# I only need to look at the right-hand side of this and if names
# is NOT empty.

if isinstance(names, (tuple, list)) and names and isinstance(names[0], str):
#                                                 ^
# If I'm evaluating this expression, it is because names is either a
# list or a tuple AND it is not empty, therefore I can index safely into it
# with names[0].

This flat if statement is much better than the completely nested version:

if isinstance(names, (tuple, list)):
if names:
if isinstance(names[0], str):
pass

Of course, you might need the nested version if, at different points, you might need to do different things depending on what happens. For example, suppose you want to raise an error if the list/tuple is empty. In that case, you would need the nested version:

if isinstance(names, (tuple, list)):
if names:
if isinstance(names[0], str):
pass
else:
raise ValueError("Empty names :(")

Can you understand why this if statement I just wrote is different from the two following alternatives?

# Can I put and names together with the first check?
if isinstance(names, (tuple, list)) and names:
if isinstance(names[0], str):
pass
else:
raise ValueError("Empty names..? :(")

# What if I put it together with the second isinstance check?
if instance(names, (tuple, list)):
if names and isinstance(names[0], str):
pass
else:
raise ValueError("Empty names..? :(")

If this is a silly exercise for you, sorry about that! I just want you to be aware of the fact that when you have many Boolean conditions, you need to be careful when checking specific configurations of what is True and what is False.

## Define default values

### How it works

If you've been skimming this article, just pay attention to this section right here. This, right here, is my favourite use of short-circuiting. Short-circuiting with the Boolean operator or can be used to assign default values to variables.

How does this work? This uses or and its short-circuiting functionality to assign a default value to a variable if the current value is falsy. Here is an example:

greet = input("Type your name >> ") or "there"
print(f"Hello, {greet}!")

Try running this example and press Enter without typing anything. If you do that, input returns an empty string "", which is falsy. Therefore, the operator or sees the falsy value on its left and needs to evaluate the right operand to determine the final value of the expression. Because it evaluates the right operand, it is the right value that is returned, and "there" is assigned to greet.

### Ensuring a list is not empty

Now that we've seen how this mechanism to assign default values works, let us take a look at a couple of usage examples from the Python Standard Library.

We start with a simple example from the collections module, specifically from the implementation of the ChainMap object:

# From Lib/collections/__init__.py in Python 3.9.2
class ChainMap(_collections_abc.MutableMapping):
''' A ChainMap groups multiple dicts (or other mappings) together
[docstring cut for brevity]
'''

def __init__(self, *maps):
'''Initialize a ChainMap by setting *maps* to the given mappings.
If no mappings are provided, a single empty dictionary is used.

'''
self.maps = list(maps) or [{}]          # always at least one map

This ChainMap object allows you to combine multiple mappings (for example, dictionaries) into a single mapping that combines all the keys and values.

>>> import collections
>>> a = {"A": 1}
>>> b = {"B": 2, "A": 3}
>>> cm = collections.ChainMap(a, b)
>>> cm["A"]
1
>>> cm["B"]
2

The assignment that we see in the source code ensures that self.maps is a list of, at least, one empty mapping. If we give no mapping at all to ChainMap, then list(maps) evaluates to [], which is falsy, and forces the or to look at its right operand, returning [{}]: this produces a list with a single dictionary that has nothing inside.

### Default value for a mutable argument

I'll share another example with you, now. This example might look like the same as the one above, but there is a nice subtlety here.

First, the code:

# From Lib/cgitb.py in Python 3.9.2
class Hook:
"""A hook to replace sys.excepthook that shows tracebacks in HTML."""

def __init__(self, display=1, logdir=None, context=5, file=None,
format="html"):
self.display = display          # send tracebacks to browser if true
self.logdir = logdir            # log tracebacks to files if not None
self.context = context          # number of source code lines per frame
self.file = file or sys.stdout  # place to send the output
self.format = format

This code comes from the cgitb module and defines sys.stdout to be the default value for the self.file variable. The definition of the __init__ function has file=None as a keyword argument also with a default value of None, so why don't we just write file=sys.stdout in the first place?

The problem is that sys.stdout can be a mutable object, and therefore, using file=sys.stdout as a keyword argument with a default value is not going to work as you expect. This is easier to demonstrate with a list as the default argument, although the principle is the same:

>>> def append(val, l=[]):
...   l.append(val)
...   print(l)
...
>>> append(3, [1, 2])
[1, 2, 3]
>>> append(5)
[5]
>>> append(5)
[5, 5]
>>> append(5)
[5, 5, 5]

Notice the three consecutive calls append(5). We would expect the three calls to behave the same way, but because a list is a mutable object, the three consecutive calls to append add the values to the default value itself, that started out as an empty list but keeps growing.

## Find witnesses in a sequence of items

As the final usage example of short-circuiting, I'll share something really neat with you.

If you use assignment expressions and the walrus operator := together with generator expressions, we can use the fact that all and any also short-circuit in order to look for âwitnessesâ in a sequence of elements.

If we have a predicate function predicate (a function that returns a Boolean value) and if we have a sequence of values, items, we could use

any(predicate(item) for item in items)

to check if any element(s) in items satisfy the predicate function.

If we modify that to be

any(predicate(witness := item) for item in items)

Then, in case any item satisfies the predicate function, witness will hold its value!

For example, if items contains many integers, how do we figure out if there are any odd numbers in there and how do we print the first one?

items = [14, 16, 18, 20, 35, 41, 100]
any_found = False
for item in items:
any_found = item % 2
if any_found:
print(f"Found odd number {item}.")
break

# Prints 'Found odd number 35.'

This is one alternative. What other alternatives can you come up with?

Now, compare all those with the following:

items = [14, 16, 18, 20, 35, 41, 100]
is_odd = lambda x: x % 2
if any(is_odd(witness := item) for item in items):
print(f"Found odd number {witness}.")

# Prints 'Found odd number 35.'

Isn't this neat?

# Conclusion

Here's the main takeaway of this Pydon't, for you, on a silver platter:

âBe mindful when you order the left and right operands to the and and or expressions, so that you can make the most out of short-circuiting.â

This Pydon't showed you that:

• and and or return the value of one of its operands, and not necessarily a Boolean value;
• both Boolean operators short-circuit:
• and only evaluates the right operand if the left operand is truthy;
• or only evaluates the right operand if the left operand is falsy;
• the built-in functions all and any also short-circuit;
• short-circuiting also happens in chained comparisons, because those contain an implicit and operator;
• using short-circuiting can save you a lot of computational time;
• nested structures of if statements can, sometimes, be flattened and simplified if we use short-circuiting with the correct ordering of the conditions;
• it is customary to use short-circuiting to test some preconditions before applying a test to a variable;
• another great use-case for short-circuiting is to assign default values to variables and function arguments, especially if the default value is a mutable value; and
• short-circuiting, together with the walrus operator :=, can be used to find a witness value with respect to a predicate function.

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