Why are non-mutating algorithms simpler to write in Python?

Let's say you've got the list [-10, 3, -2, 14, 5], and you want the list filtered to just the non-negative values. You could do this by mutating the list:

for i, e in enumerate(lst[:]):
    if e < 0:
        del lst[i]

Or you could do it by making a new list:

newlst = []
for e in lst:
    if e >= 0:
        newlst.append(e)
return newlst

But you can always transform a "l=[] … l.append … return l" into a list comprehension:

return [e for e in lst if e >= 0]

Or, in many cases, a call to map, filter, or something out of itertools:

return filter(lambda e: e >= 0, lst)

But there's no equivalent way to simplify the mutating version. You have to write an explicit for loop. You also have to make a copy of the list, because you can't change the shape of a collection while iterating over it (this wouldn't be the case for a map-equivalent that just changed some of the values without inserting or deleting any). And you have to use enumerate and explicitly refer to lst[i] to change anything, rather than just referring to e.

There are a few shortcuts for mutating lists, such as remove, but nothing flexible or general.

And you can, of course, abuse the non-mutating tools in some cases, but there are also many cases where this doesn't work, and it's clear that this is considered abuse even when it does work. (Consider the fact that map and filter now return iterators instead of lists, which means if you want to abuse them for their side-effects, nothing actually happens, unless you borrow the consume function from the itertools docs recipes and pass the iterator into a deque(maxlen=0) or something equivalent.)

It's not that these operations would be hard to define, explain, or implement. Look at C++, which has matched pairs for everything. Instead of map and filter, they have transform and remove_if_copy—and, right alongside those, they have for_each and remove_if, which mutate in place. In fact, just about every algorithm in <algorithm> has an in-place variant.

Of course you could point out that C++ is designed for people who want programming to be difficult. After all, they call all of the non-mutating algorithms "modifying", and some but not all of the mutating-in-place algorithms "non-modifying". And sometimes the matched pairs are "foo" vs. "foo_copy", other times "inplace_foo" vs. "foo", and other times completely unrelated names like "for_each" vs. "transform".

But the point is, Python could easily have a map_inplace or for_each, extend replace with replace_if, or allow one-liner list modifiers using comprehension-style syntax. And it doesn't. Why?

Well, first, there's historical reasons. Python borrowed map, filter, and friends from mostly-immutable mostly-functional languages, and list comprehensions from a purely-immutable pure-functional language. The kind of people who wanted to use them didn't want to mutate anything. Meanwhile, C++, borrowed the same ideas from the same sources but redesigned them to fit into a more C-ish imperative-mutating style of programming, but pretty much nobody used them, so nobody was going to borrow them into other languages.

But after years of use, list comprehensions, generator expressions, map, etc. have become common, idiomatic Python that even novices understand—even though those novices mostly think of mutating algorithms first as a matter of course. So, why don't they all demand mutating equivalents? Why didn't Python 3 include them?

There's a conservative explanation. Look at the two functions at the top of the page. The first one does take 3 lines to do something that could be done in 1, but they're all very simple lines that say exactly what they do, and there's really nothing that gets in the way of reading the actual logic. The second one, however, is half boilerplate. Only half of it says what it does, and the other half is unimportant details about how it does it. Reducing line count for its own sake is not worth changing syntax for; reducing boilerplate to make code more readable is.

There's also a more radical explanation. If you look at the major changes in Python 3, other than the Unicode stuff, most of them are about replacing list-based code with iterator-based code. And there's a good reason for this: When you write everything in terms of iterator transformations, you can pipeline functions value-by-value instead of collection-by-collection. And that turns out to be pretty cool, for a number of reasons, enough to deserve its own separate post. And that's enough to add tools to help the non-mutating style.

It's been more than a decade since Typical Programmer Greg Jorgensen taught the word about Abject-Oriented Programming.

Much of what he said still applies, but other things have changed. Languages in the Abject-Oriented space have been borrowing ideas from another paradigm entirely—and then everyone realized that languages like Python, Ruby, and JavaScript had been doing it for years and just hadn't noticed (because these languages do not require you to declare what you're doing, or even to know what you're doing). Meanwhile, new hybrid languages borrow freely from both paradigms.

This other paradigm—which is actually older, but was largely constrained to university basements until recent years—is called Functional Addiction.

A Functional Addict is someone who regularly gets higher-order—sometimes they may even exhibit dependent types—but still manages to retain a job.

Retaining a job is of course the goal of all programming. This is why some of these new hybrid languages, like Rust, check all borrowing, from both paradigms, so extensively that you can make regular progress for months without ever successfully compiling your code, and your managers will appreciate that progress. After all, once it does compile, it will definitely work.

Closures

It's long been known that Closures are dual to Encapsulation.

As Abject-Oriented Programming explained, Encapsulation involves making all of your variables public, and ideally global, to let the rest of the code decide what should and shouldn't be private.

Closures, by contrast, are a way of referring to variables from outer scopes. And there is no scope more outer than global.

Immutability

One of the reasons Functional Addiction has become popular in recent years is that to truly take advantage of multi-core systems, you need immutable data, sometimes also called persistent data.

Instead of mutating a function to fix a bug, you should always make a new copy of that function. For example:

function getCustName(custID)
{
    custRec = readFromDB("customer", custID);
    fullname = custRec[1] + ' ' + custRec[2];
    return fullname;
}

When you discover that you actually wanted fields 2 and 3 rather than 1 and 2, it might be tempting to mutate the state of this function. But doing so is dangerous. The right answer is to make a copy, and then try to remember to use the copy instead of the original:

function getCustName(custID)
{
    custRec = readFromDB("customer", custID);
    fullname = custRec[1] + ' ' + custRec[2];
    return fullname;
}

function getCustName2(custID)
{
    custRec = readFromDB("customer", custID);
    fullname = custRec[2] + ' ' + custRec[3];
    return fullname;
}

This means anyone still using the original function can continue to reference the old code, but as soon as it's no longer needed, it will be automatically garbage collected. (Automatic garbage collection isn't free, but it can be outsourced cheaply.)

Higher-Order Functions

In traditional Abject-Oriented Programming, you are required to give each function a name. But over time, the name of the function may drift away from what it actually does, making it as misleading as comments. Experience has shown that people will only keep once copy of their information up to date, and the CHANGES.TXT file is the right place for that.

Higher-Order Functions can solve this problem:

function []Functions = [
    lambda(custID) {
        custRec = readFromDB("customer", custID);
        fullname = custRec[1] + ' ' + custRec[2];
        return fullname;
    },
    lambda(custID) {
        custRec = readFromDB("customer", custID);
        fullname = custRec[2] + ' ' + custRec[3];
        return fullname;
    },
]

Now you can refer to this functions by order, so there's no need for names.

Parametric Polymorphism

Traditional languages offer Abject-Oriented Polymorphism and Ad-Hoc Polymorphism (also known as Overloading), but better languages also offer Parametric Polymorphism.

The key to Parametric Polymorphism is that the type of the output can be determined from the type of the inputs via Algebra. For example:

function getCustData(custId, x)
{
    if (x == int(x)) {
        custRec = readFromDB("customer", custId);
        fullname = custRec[1] + ' ' + custRec[2];
        return int(fullname);
    } else if (x.real == 0) {
        custRec = readFromDB("customer", custId);
        fullname = custRec[1] + ' ' + custRec[2];
        return double(fullname);
    } else {
        custRec = readFromDB("customer", custId);
        fullname = custRec[1] + ' ' + custRec[2];
        return complex(fullname);
    }
}

Notice that we've called the variable x. This is how you know you're using Algebraic Data Types. The names y, z, and sometimes w are also Algebraic.

Type Inference

Languages that enable Functional Addiction often feature Type Inference. This means that the compiler can infer your typing without you having to be explicit:

function getCustName(custID)
{
    // WARNING: Make sure the DB is locked here or
    custRec = readFromDB("customer", custID);
    fullname = custRec[1] + ' ' + custRec[2];
    return fullname;
}

We didn't specify what will happen if the DB is not locked. And that's fine, because the compiler will figure it out and insert code that corrupts the data, without us needing to tell it to!

By contrast, most Abject-Oriented languages are either nominally typed—meaning that you give names to all of your types instead of meanings—or dynamically typed—meaning that your variables are all unique individuals that can accomplish anything if they try.

Memoization

Memoization means caching the results of a function call:

function getCustName(custID)
{
    if (custID == 3) { return "John Smith"; }
    custRec = readFromDB("customer", custID);
    fullname = custRec[1] + ' ' + custRec[2];
    return fullname;
}

Non-Strictness

Non-Strictness is often confused with Laziness, but in fact Laziness is just one kind of Non-Strictness. Here's an example that compares two different forms of Non-Strictness:

/****************************************
*
* TO DO:
*
* get tax rate for the customer state
* eventually from some table
*
****************************************/
// function lazyTaxRate(custId) {}

function callByNameTextRate(custId)
{
    /****************************************
    *
    * TO DO:
    *
    * get tax rate for the customer state
    * eventually from some table
    *
    ****************************************/
}

Both are Non-Strict, but the second one forces the compiler to actually compile the function just so we can Call it By Name. This causes code bloat. The Lazy version will be smaller and faster. Plus, Lazy programming allows us to create infinite recursion without making the program hang:

/****************************************
*
* TO DO:
*
* get tax rate for the customer state
* eventually from some table
*
****************************************/
// function lazyTaxRateRecursive(custId) { lazyTaxRateRecursive(custId); }

Laziness is often combined with Memoization:

function getCustName(custID)
{
    // if (custID == 3) { return "John Smith"; }
    custRec = readFromDB("customer", custID);
    fullname = custRec[1] + ' ' + custRec[2];
    return fullname;
}

Outside the world of Functional Addicts, this same technique is often called Test-Driven Development. If enough tests can be embedded in the code to achieve 100% coverage, or at least a decent amount, your code is guaranteed to be safe. But because the tests are not compiled and executed in the normal run, or indeed ever, they don't affect performance or correctness.

Conclusion

Many people claim that the days of Abject-Oriented Programming are over. But this is pure hype. Functional Addiction and Abject Orientation are not actually at odds with each other, but instead complement each other.

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