## Structural pattern matching anti-patterns | Pydon't 🐍

Structural pattern matching is coming in Python 3.10 and the previous Pydon't explored some interesting use cases for the new match statement. This article explores situations for which match isn't the answer.

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

# Introduction

Structural pattern matching is coming to Python, and while it may look like a plain switch statement like many other languages have, Python's match statement was not introduced to serve as a simple switch statement.

In this article I explored plenty of use cases for the new match statement, and in this blog post I will try to explore some use cases for which a match is not the answer. This article will assume you know how structural pattern matching works in Python, so if you are unsure how that works feel free to read my “Pattern matching tutorial for Pythonic code”.

# There should be only one obvious way to do it

As per the Zen of Python,

“There should be one-- and preferably only one --obvious way to do it.”

The introduction of the match statement may seem to violate this principle... However, you have to remember that the point of the match statement is not to serve as a basic switch statement, as we already have plenty of alternatives for that – if match was supposed to be a simple switch, then it would probably be called “switch” and not “match”. No, the point of the match statement is to do structural pattern matching, so there's plenty of basic types of matching and casing that can be done with the traditional tools that we have been using up until Python 3.10.

Below I will share some of these tools with you, and I'll try to describe the situations in which they are helpful. Of course, this is much easier to understand with code so there will be plenty of code examples along the way.

# A short and sweet if statement

The Collatz conjecture is a mathematical conjecture that says that the following function terminates for any positive integer given as input:

def collatz_path(n):
path = [n]
while n != 1:
match n % 2:
case 0:
n //= 2
case 1:
n = 3*n + 1
path.append(n)
return path

Which gives the following two example outputs:

>>> collatz_path(8)
[8, 4, 2, 1]
>>> collatz_path(15)
[15, 46, 23, 70, 35, 106, 53, 160, 80, 40, 20, 10, 5, 16, 8, 4, 2, 1]

If we look at the usage of match above, we see it basically served as a simple switch to match either 0 or 1, the only two values that the operation n % 2 could result in for a positive integer n. Notice that if we use a plain if we can write exactly the same code and save one line of code:

def collatz_path(n):
path = [n]
while n != 1:
if n % 2:
n = 3*n + 1
else:
n //= 2
path.append(n)
return path

We saved one line of code and reduced the maximum depth of our indentation: with the match we had code that was indented four times, whereas the implementation with the if only has three levels of depth. When you only have a couple of options and you are checking for explicit equality, a short and sweet if statement is most likely the way to go.

# Be smart(er)

Sometimes you will feel like you have to list a series of cases and corresponding values, so that you can map one to the other. However, it might be the case that you could make your life much simpler by looking for an alternative algorithm or formula and implementing that instead. I'll show you an example.

In case you never heard of it, Rule 30 is an “elementary cellular automaton”. You can think of it as a rule that receives three bits (three zeroes/ones) and produces a new bit, depending on the three bits it received. Automatons are really, really, interesting, but discussing them is past the point of this article. Let us just look at a possible implementation of the “Rule 30” automaton:

def rule30(bits):
match bits:
case 0, 0, 0:
return 0
case 0, 0, 1:
return 1
case 0, 1, 0:
return 1
case 0, 1, 1:
return 1
case 1, 0, 0:
return 1
case 1, 0, 1:
return 0
case 1, 1, 0:
return 0
case 1, 1, 1:
return 0

This seems like a sensible use of the match statement, except that we just wrote 16 lines of code... Ok, you are right, let us put together the rules that return the same values, that should make the code shorter:

def rule30(bits):
match bits:
case 0, 0, 0 | 1, 0, 1 | 1, 1, 0 | 1, 1, 1:
return 0
case 0, 0, 1 | 0, 1, 0 | 0, 1, 1 | 1, 0, 0:
return 1

Yup, much better. But now we have four options on each case, and I have to squint to figure out where each option starts and ends, and the long strings of zeroes and ones aren't really that pleasant to the eye... Can we make it better..?

With just a little bit of research you can find out that the “Rule 30” can be written as a closed formula that depends on the three input bits, which means we don't have to match the input bits with all the possible inputs, we can just compute the output:

def rule30(bits):
p, q, r = bits
return (p + q + r + q*r) % 2

You might argue that this formula obscures the relationship between the several inputs and their outputs. You are right in principle, but having the explicit “Rule 30” written out as a match doesn't tell you much about why each input maps to each output either way, so why not make it short and sweet?

# Basic mappings

## Getting from dictionaries

There are many cases in which you just want to take a value in and map it to something else. As an example, take this piece of code that takes an expression and writes it in prefix notation:

import ast

def prefix(tree):
match tree:
case ast.Expression(expr):
return prefix(expr)
case ast.Constant(value=v):
return str(v)
case ast.BinOp(lhs, op, rhs):
match op:
sop = "+"
case ast.Sub():
sop = "-"
case ast.Mult():
sop = "*"
case ast.Div():
sop = "/"
case _:
raise NotImplementedError()
return f"{sop} {prefix(lhs)} {prefix(rhs)}"
case _:
raise NotImplementedError()

print(prefix(ast.parse("1 + 2 + 3", mode="eval")))     # + + 1 2 3
print(prefix(ast.parse("2**3 + 6", mode="eval"))       # + * 2 3 6
# Final one prints '- + 1 * 2 3 / 5 7', take a moment to grok it.
print(prefix(ast.parse("1 + 2*3 - 5/7", mode="eval")))

Notice the inner match to convert the op inside a BinOp to a string? For starters, that nested match takes up too much vertical space and distracts us from what really matters, which is the traversal of the recursive structure of the tree. This means we could actually refactor that bit as a utility function:

import ast

def op_to_str(op):
match op:
sop = "+"
case ast.Sub():
sop = "-"
case ast.Mult():
sop = "*"
case ast.Div():
sop = "/"
case _:
raise NotImplementedError()
return sop

def prefix(tree):
match tree:
case ast.Expression(expr):
return prefix(expr)
case ast.Constant(value=v):
return str(v)
case ast.BinOp(lhs, op, rhs):
return f"{op_to_str(op)} {prefix(lhs)} {prefix(rhs)}"
case _:
raise NotImplementedError()

print(prefix(ast.parse("1 + 2 + 3", mode="eval")))     # + + 1 2 3
print(prefix(ast.parse("2**3 + 6", mode="eval"))       # + * 2 3 6
# Final one prints '- + 1 * 2 3 / 5 7', take a moment to grok it.
print(prefix(ast.parse("1 + 2*3 - 5/7", mode="eval")))

This makes it easier to read and interpret the prefix function, but now we have another problem that really annoys me: a simple but long function, the op_to_str function. For every type of operator you support, your function grows by two lines... If you replace the match with a chain of if and elif statements you only save one line at the top...

The fix I suggested in the original article was using a dictionary to map the type of op to its string representation:

def op_to_str(op):
ops = {
ast.Sub: "-",
ast.Mult: "*",
ast.Div: "/",
}
return ops.get(op.__class__, None)

This usage pattern of a dictionary is quite common in Python, using the get method to compute the mapping of a value to another value. In case you are wondering, you can use the second argument of the get function to provide for a default value, which might be useful if the dictionary hasn't listed every single possible value or in case you want to have a fallback value.

## getattr

Another useful mechanism that we have available is the getattr function, which is part of a trio of Python built-in functions: hasattr, getattr and setattr.

I am writing an APL interpreter called RGSPL, and there is a function named visit_F where I need to map APL symbols like +, -, and ⍴ to the corresponding Python function that implements it. These Python functions, implementing the behaviour of the symbols, live in the functions.py file. If I were using a match statement, here is what this visit_F could look like:

import functions

def visit_F(self, func):
"""Fetch the callable function."""

name = func.token.type.lower()  # Get the name of the symbol.
match name:
case "plus":
function = functions.plus
case "minus":
function = functions.minus
case "reshape":
function = functions.reshape
case _:
function = None
if function is None:
raise Exception(f"Could not find function {name}.")
return function

This is a similar problem to the one I showed above, where we wanted to get a string for each type of operator we got, so this could actually be written with the dictionary mapping. I invite you to do it, as a little exercise.

However, here's the catch: I have still a long way to go in my RGSPL project, and I already have a couple dozen of those primitives, so my match statement would be around 40 lines long, if I were using that solution, or 20 lines long if I were using the dictionary solution, with a key, value pair per line.

Thankfully, Python's getattr can be used to get an attribute from an object, if I have the name of that attribute. It is no coincidence that the value of the name variable above is supposed to be exactly the same as the name of the function defined inside functions.py:

import functions

getattr(functions, "plus", None)        # returns functions.plus
getattr(functions, "reshape", None)     # returns functions.reduce
getattr(functions, "fasfadf", None)     # returns None

With the getattr function that Python provides, my visit_F stays with a constant size, regardless of how many functions I add to the functions.py file:

def visit_F(self, func):
"""Fetch the callable function."""

name = func.token.type.lower()      # Get the name of the symbol.
function = getattr(functions, name, None)
if function is None:
raise Exception(f"Could not find function {name}.")
return function

The getattr function can also be used to get attributes from an instance of a class, e.g.,

class Foo:
def __ini__(self, a, b):
self.a = a
self.b = b

foo = Foo(3, 4)
print(getattr(foo, "a"))    # prints 3
bar = Foo(10, ";")
print(getattr(bar, ";"))    # prints ';'

This goes to show that it is always nice to know the tools you have at your disposal. Not everything has very broad use cases, but that also means that the more specialised tools are the ones that make the most difference when they are brought in.

Speaking of knowing your tools, the last use case in this article for which match is a bad alternative is related to calling different functions when your data has different types.

## Single-dispatch generic functions

If you have programming experience in a programming language like Java, you will be familiar with the concept of overloading a function: you implement the same function several times, but you get to specify the behaviour of the function for different types of arguments and/or number of arguments.

For example, you might want to implement a function to pretty-print a series of different types of objects:

def pretty_print(arg):
if isinstance(arg, complex):
print(f"{arg.real} + {arg.imag}i")
elif isinstance(arg, (list, tuple)):
for i, elem in enumerate(arg):
print(i, elem)
elif isinstance(arg, dict):
for key, value in arg.items():
print(f"{key}: {value}")
else:
print(arg)

Which then works like so:

>>> pretty_print(3)
3
>>> pretty_print([2, 5])
0 2
1 5
>>> pretty_print(3+4j)
3.0 + 4.0i

You can see that the branching introduced by the if statement is merely to separate the different types that the arg could have, and while the handling logic might be different, the final purpose is always the same: to pretty-print an object. But what if the code to handle each type of argument took 10 or 20 lines? You would be getting a really long function with what would essentially be embedded subfunctions.

You can separate all these subfunctions by making use of the functools.singledispatch decorator:

import functools

@functools.singledispatch
def pretty_print(arg):
print(arg)

@pretty_print.register(complex)
def _(arg):
print(f"{arg.real} + {arg.imag}i")

@pretty_print.register(list)
@pretty_print.register(tuple)
def _(arg):
for i, elem in enumerate(arg):
print(i, elem)

@pretty_print.register(dict)
def _(arg):
for key, value in arg.items():
print(f"{key}: {value}")

And this can then be used exactly like the original function:

>>> pretty_print(3)
3
>>> pretty_print([2, 5])
0 2
1 5
>>> pretty_print(3+4j)
3.0 + 4.0i

The pretty_print example isn't the best example because you spend as many lines decorating as in defining the actual subfunctions, but this shows you the pattern that you can now be on the lookout for. You can read more about singledispatch in the docs.

# Conclusion

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

“The new match statement is great, but that does not mean the match statement will be the best alternative always and, in particular, the match statement is generally being misused if you use it as a simple switch.”

This Pydon't showed you that:

• match isn't necessarily always the best way to implement control flow;
• short and basic match statements could be vanilla if statements;
• sometimes there is a way to compute what you need, instead of having to list many different cases and their respective values;
• built-in tools like dict.get and getattr can also be used to fetch different values depending on the matching key; and
• you can use functools.singledispatch when you need to execute different subfunctions when the input has different types.

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