Why you don't want to dynamically create variables

(Someone pointed out to me that Ned Batchelder has a similar post called Keep data out of your variable names. As usual for him, he's got a very concise answer that covers everything that matters in only a few paragraphs, so you might want to just read that.)

One of the most common Python questions on StackOverflow is some variation of "How do I create a bunch of variables in a loop?"

The answer is: You don't.

You can…

The direct answer is simple. Depending on whether you have names or numbers, it's one of the following:

    for name in names:
        globals()[name] = 0

    for i in range(10):
        globals()['variable{}'.format(i)] = 0

If you're trying to create function locals, builtins, class attributes, or instance attributes rather than globals, it's slightly different, but no harder. (And most people who want this want to create globals.)

But you shouldn't

But the right answer is almost always that you don't want to do that.

Why not?

If you want to access the variables statically, you must have the names statically. So just create them statically:

    spam = eggs = beans = bacon = sausage = lobster = 0

But that's obvious, and you probably knew that. Presumably you want to access the variables dynamically as well. Maybe you're getting a name from the user, or computing a number with some mathematical expression.

So, every time you want to access variable name or #n, do you want to do something like this:

    value = globals()[name]
    value = globals()['variable{}'.format(n)]

Ouch! If you just used a list or dict in the first place:

    variables = {name: 0 for name in names]
    variables = [0 for _ in range(10)]

… you could just access them like this:

    value = variables[name]
    value = variables[n]

But what about…?

Even in the rare cases somewhere in between the static and dynamic cases, you _still_ usually don't want to create variables dynamically.

For example, let's say you're building a bunch of values because you're going to hit one of 20 randomly-chosen code paths. Each of those code paths accesses its variables statically, but you don't know which one you're going to take, so you need to create the variables dynamically. Using a dict means those 20 code paths are all going to be a bit clumsy. For example, instead of this:

    if cmd == 'h':
        return (variable_a**2 + variable_b**2) ** .5

You'd have:

    if cmd == 'h':
        return (variables['a']**2 + variables['b']**2) ** .5

Ick. If you're got a static name, you don't want to look it up dynamically.

The solution here is to create a namespace by, e.g., using collections.namedtuple:

    variables = namedtuple('Variables', names)._make(0 for _ in names)

Now, you can access them statically:

    if cmd == 'h':
        return (variables.a**2 + variables.b**2) ** .5

So why can I do it?

Why does Python give you mutable access to globals and locals (and let you pass substitute globals and locals to functions where it matters)?

Because "almost always" isn't the same as "always". What if you want to build a debugger, interactive interpreter, or in-game console? Or a bridge library like ctypes, win32com, or pyobjc?

More generally, Python doesn't try to prevent you from shooting yourself in the foot; it just tries to make the obvious way to do things the best way. Just like exec, getframe, marshal, etc., globals is there when you need it—but if you think you need it, your first step should be to make sure you really do, and your second step should be to make doubly sure.

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.