Arguments and parameters

A lot of people—not just novices—mix up parameters and arguments, especially when it comes to things like how default-valued parameters and keyword arguments, or argument unpacking and variable parameters.

The FAQ has a section called What is the difference between arguments and parameters, which explains the basics:

Parameters are defined by the names that appear in a function definition, whereas arguments are the values actually passed to a function when calling it. Parameters define what types of arguments a function can accept.

PEP 3102 explains the current design. The glossary entries for argument and parameter are also helpful. And the tutorial also has nice sections on Defining Functions, which explain things in loose terms. However, to actually understand how it works, you need to read the reference documentation on Calls and Function definitions, and some of the terminology is only defined in the middle of the inspect module documentation.

However, putting it all together is a little difficult.

The explanations below are all for Python 3.x, but there is a brief overview of the differences between 3.x and 2.x at the end.

PEP 3102 uses different terminology from the reference documentation and the inspect module; I've chosen to go with the latter.

Parameters

Parameters are part of a function definition (def or lambda). There are five different kinds of parameters:

positional-or-keyword: Normal parameters in a function definition, with or without default values.

Each has a name and an index, and can accept a positional argument with the same index, or a keyword argument with the same name, or (if it has a default value) nothing.
Technically, every parameter before the first bare *, var-positional, or var-keyword is a positional-or-keyword parameter.

positional-only: Only found in builtin/extension functions.

Each has a name and an index, but only accepts positional arguments with the same index.

var-positional: This is the *args.

This accepts a sequence consisting of all positional arguments whose index is larger than any positional-or-keyword or positional-only parameter. (Note that you can also specify a bare * here. In that case, you don't take variable positional arguments. You do this to set off keyword-only from positional-or-keyword parameters.)

keyword-only: These are parameters that come after a * or *args, with or without default values.

Each has a name only, and accepts only keyword arguments with the same name.
Technically, every parameter after the first bare * or var-positional, but before the var-keyword (if any), is a keyword-only parameter.

var-keyword: This is the **args.

This accepts a mapping consisting of all keyword arguments whose name does not match any positional-or-keyword or keyword-only parameter.

Arguments

Arguments are part of a function call. There are four different kinds of arguments:

positional: Arguments without a name.

Each is matched to the positional-or-keyword or positional-only parameter with the same index, or to the var-positional parameter if there is no matching index (or, if there is no var-positional parameter, it's an error if there is no match).

keyword: Arguments with a name.

Each is matched to the postional-or-keyword or keyword-only parameter with the same name, or to the var-keyword parameter if there is no matching name (or, if there is no var-keyword parameter, it's an error if there is no match).

packed positional: An iterator preceded by *.

The iterator is unpacked, and the values treated as separate positional arguments.

packed keyword: A mapping preceded by **.

The mapping is iterated, and the key-value pairs treated as separate keyword arguments.

There is no direct connection between parameters with a default value and keyword arguments. You can pass keyword arguments to parameters without default values, or positional arguments to parameters with them.

Similarly, there is no direct connection between a *args in a function call and a *args in a function definition. A packed positional argument with three values might be unpacked into the last three positional-or-keyword parameters, or all three may be extended to the last two normal positional arguments and passed together to the var-positional, or its first two values may be unpacked into positional-or-keyword parameters and the last into the var-positional.

Examples

In the following example:

    >>> def func(spam, eggs=1, *args, foo, bar=2, **kwargs):
    ...     print(spam, eggs, args, foo, bar, kwargs)
    >>> func(1, 2, 3, 4, foo=5, bar=6, baz=7)
    1 2 (3, 4) 5 6 {'baz': 7}
    >>> func(baz=1, bar=2, foo=3, eggs=4, spam=5)
    5 4 () 3 2 {'baz': 1}
    >>> func(foo=1, spam=2)
    2 1 () 1 2 {}

spam is a positional-or-keyword parameter with no default value. This means any call must pass either at least one positional argument (as in the first call), or a keyword argument with name "spam" (as in the other two).

eggs is a positional-or-keyword parameter with a default value. This means a call may pass either at least two positional arguments (as in the first call), or a keyword argument with name "eggs" (as in the second), or neither (as in the third).

args is a var-positional parameter. Because there is such a parameter, it's legal to pass more than two positional arguments, in which case it will get all of the excess (as in the first call), but of course it's not required (as in the other two).

foo is a keyword-only parameter with no default value. This means any call must pass a keyword argument with the name "foo" (as in all three calls).

bar is a keyword-only argument with a default value. This means a call may pass a keyword argument with the name "bar" (as in the first two calls), or not (as in the third).

kwargs is a var-keyword parameter. Because there is such a parameter, it's legal to pass keyword arguments whose names don't match any parameter (as in the first two calls), but of course it's not required (as in the third).

With the same function from above:

    >>> a = [2,3,4]
    >>> d = {'foo': 5, 'baz': 6}
    >>> func(1, *a, **d)
    1 2 (3, 4) 5 2 {'baz': 6}
    >>> func(1, 2, 3, 4, foo=5, baz=6)
    1 2 (3, 4) 5 2 {'baz': 6}

In the first call, the a iterable is unpacked into three positional arguments, and the **d is unpacked into keyword arguments named "foo" and "baz". So, this has the exact same effect as the second call.

Python 2.x

Most of the above is true for Python 2.x as well, except:

There are no keyword-only arguments between *args and **kwargs.
You cannot use a bare * to mean no variable-positional parameter.

However, the documentation is not as thorough, and some edge cases may be slightly different.

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.