Recently, there have been a few proposals to change Python's syntax to make it easier to avoid break and continue statements.

The reasoning seems to be that many people are taught never to use break and continue, or to only have a single break in any loop. Some of these people are in fact forced to follow these rules for class assignments.

These rules are ludicrous. The stdlib has hundreds of break and continue statements. There are dozens of them in the official tutorial, the library reference docs, Guido's blogs, etc. The reasons for avoiding break and continue in other languages don't apply to Python, and many of the ways people use to avoid them don't even exist in Python. Using break and continue appropriately is clearly Pythonic.

The python-ideas community tracked these rules back to some misguided attempts to apply the rules from MISRA-C to Python.

It should be obvious that C and Python are very different languages, with different appropriate idioms, so applying MISRA-C to Python is insanely stupid. But, in case anyone doesn't get it, I've gone through the requirements and recommendations in MISRA-C 1998. By my count, more than half of them don't even mean anything in Python (e.g., rules about switch statements or #define macros), and more than half of the rest are bad rules that would encourage non-Pythonic code.

I've paraphrased them, both to avoid violating the copyright on the document, and to express them in Python terms:
  • 8. req: No unicode, only bytes, especially in literals. (In other words, '\u5050' is bad; b'\xe5\x81\x90' is good.)
  • 12. rcm: Don't use the same identifier in multiple namespaces. (For example, io.open and gzip.open should not have the same name, nor should two different classes ever have members or methods with the same name.)
  • 13. rcm: Never use int when you can use long. (Only applies to Python 2.x.)
  • 18. rcm: Suffix numeric literals. (Only applies to Python 2.x.)
  • 20. req: Declare all variables and functions at the top of a module or function.
  • 31. req: Use curly braces in all initializers. (Sorry, no lists allowed, just dicts and sets.)
  • 33. req: Never use a user-defined function call on the right side of logical and/or.
  • 34. req: Never use anything but a primary expression on either side of logical and/or.
  • 37. req.: Never use bitwise operators on signed types (like int).
  • 47. rcm: Never rely on operator precedence rules.
  • 48. rcm: Use explicit conversions when performing arithmetic between multiple types.
  • 49. rcm: Always test non-bool values against False instead of relying on truthiness.
  • 53. req: All statements should have a side-effect.
  • 57. req: No continue.
  • 58. req: No break.
  • 59. req: No one-liner if and loop bodies.
  • 60. rcm: No if/elif without else.
  • 67. rcm: Don't rebind the iterator in a for loop.
  • 68. req: All functions must be at file scope.
  • 69. req: Never use *args in functions.
  • 70. req: No recursion.
  • 82. rcm: Functions should have a single point of exit.
  • 83. req: No falling off the end of a function to return None.
  • 86. rcm: Always test function returns for errors.
  • 104. req: No passing functions around.
  • 118. req: Never allocate memory on the heap.
  • 119. req: Never rely on the errno in an OSError (or handle FileNotFoundError separately, etc.).
  • 121. req: Don't use locale functions.
  • 123. req: Don't use signals.
  • 124. req: Don't use stdin, stdout, etc., including print.
  • 126. req: Don't use sys.exit, os.environ, os.system, or subprocess.*.
  • 127. req: Never use Unix-style timestamps (e.g., time.time()).


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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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