How not to overuse lambda

There are two ways that some Python programmers overuse lambda. Doing this almost always mkes your code less readable, and for no corresponding benefit.

Don't use lambda for named functions

Some programmers don't understand that in Python, def and lambda define exactly the same kind of function object. (Especially since the equivalent is not true in C++, Ruby, and many other languages.)

The differences between def and lambda are:

def gives you a named function, lambda an anonymous function.
def lets you include statements in the body, lambda only an expression.
lambda can be used within an expression, def cannot.

So, when you want to, e.g., define a short callback in the middle of an async request or a GUI widget constructor, especially when it doesn't have an obvious good name, use lamdba.

But when you want to define a named function, use def.

In other words, don't write this:

iszero = lambda x: hash(x) == hash(0)

(There are other problems with that function, but let's ignore them…)

What's wrong with lambda for named functions?

Following idioms matters.
That "iszero" function up there may be bound the the module global name "iszero"—but if it shows up in a traceback, or you print it out at the interactive prompt, or use the inspect module on it, its name is actually <lambda>. That's not nearly as useful.
The def statement's syntax parallels function call syntax: "def iszero(x):" means it's called with "iszero(x)". That isn't true for "iszero = lambda x:".
If you later need to change the body to, say, add a try/except around it, you can't do that in a lambda.

But isn't lambda a lot more concise?

In exchange for dropping the 6-letter "return" keyword, and possibly a pair of parens, you've replaced a 3-letter "def" keyword with a 6-letter "lambda" keyword, and added a space and an equals sign. How much more concise do you think that's going to be? Test it yourself; you save 1-3 characters this way; that's it.

If you're thinking that lambda can go on one line and def can't, of course it can:

def iszero(x): return hash(x) == hash(0)

Sure, PEP 8 says that one-line compound statements are "generally discouraged", but it includes plenty of "Yes" examples that do exactly that; when the recommendations section of a document that itself is only a guideline specifically points out that something is just a "usually don't" rather than a hard "don't", that means something.

And, more importantly, avoiding making your code arguably unpythonic by transforming it into something definitely even more unpythonic is not helping.

And if you're just doing it to trick your linter, I shouldn't have to explain why using a linter but then tricking it is kind of pointless.

Don't use lambda when you don't need a function

In Python, there should be one, and only one, obvious way to do it. And yet we have both comprehensions and higher-order functions like map and filter. Why?

When you have an expression to map or filter with, a comprehension is the obvious way to do it.

When you have a function to map or filter with, the higher-order function is the obvious way to do it.

It's a little silly (and maybe inefficient) to wrap a function call in an expression just to avoid calling map; it's a lot sillier (and more inefficient) to wrap an expression in a function just so you _can_ call map. Compare:

vals = (x+2*y for x, y in zip(xs, ys)) # good
vals = map(lambda (x, y): x+2*y, zip(xs, ys)) # bad, and illegal in Python 3
vals = map(lambda xy: xy[0]+2*xy[1], zip(xs, ys)) # even worse, but legal

The point of your code is not calling a function that adds some numbers, it's just adding some numbers. There's no clearer way to write that than "x+2*y".

Of course the silliest of all is doing both of these things—you have a function, which you wrap in an expression which you then wrap in a function again:

vals = map(spam, vals) # good
vals = map(lambda x: spam(x), vals) # really?

Borderline cases

Sometimes, you have a function, but it may not be very obvious to all readers:

vals = (x+y for x, y in zip(xs, ys)) # good
vals = map(operator.add, zip(xs, ys)) # also good

Or the only expression that can replace a function is a higher-order expression that some readers may not grasp:

vals.sort(key=functools.partial(spam, maxval)) # decent
vals.sort(key=lambda x: spam(maxval, x)) # also decent

Here, it's really a judgment call. It depends who you expect to be reading your code, and what feels most natural to you, and maybe even what the surrounding code looks like.

Of course when you need to do something that you can't quite do with the higher-order tools that Python has provided, it's even more obviously a judgment call. You don't want to write (or install off PyPI) a right-partial, argument-flip, or compose function just to avoid one use of lambda—but to avoid 200 uses of lambda throughout a project, maybe you do.

Comprehensions only handle map and filter

As great as comprehensions are, they don't let you replace functools.reduce or itertools.dropwhile, only map and filter. If you are going to use functions like that, you don't have any choice but to wrap up your transforming expression or predicate test in a function. Of course sometimes that's a reason not to use those functions (Guido likes to say inside every reduce is a for loop screaming to get out), but sometimes it isn't. Again, each case is a judgment call.

So why do we even have lambda?

Because every time Guido suggests getting rid of it, the rest of the community shouts him down. :)

More seriously, lambda is great for exactly the kinds of examples I gave above—a short and simple button callback, or sorting key, or dropwhile predicate; doing something that's just outside the grasp of partial; etc.

Sometimes you need a function that's anonymous, trivial, and can be written in-line within an expression. And that's why we have lambda.

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.