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.

I haven't posted anything new in a couple years (partly because I attempted to move to a different blogging platform where I could write everything in markdown instead of HTML but got frustrated—which I may attempt again), but I've had a few private comments and emails on some of the old posts, so I

Looking before you leap

Python is a duck-typed language, and one where you usually trust EAFP ("Easier to Ask Forgiveness than Permission") over LBYL ("Look Before You Leap").

Background

Currently, CPython’s internal bytecode format stores instructions with no args as 1 byte, instructions with small args as 3 bytes, and instructions with large args as 6 bytes (actually, a 3-byte EXTENDED_ARG followed by a 3-byte real instruction).

If you want to skip all the tl;dr and cut to the chase, jump to Concrete Proposal.

Many people, when they first discover the heapq module, have two questions:

Why does it define a bunch of functions instead of a container type? Why don't those functions take a key or reverse parameter, like all the other sorting-related stuff in Python? Why not a type?

At the abstract level, it'

Currently, in CPython, if you want to process bytecode, either in C or in Python, it’s pretty complicated.

The built-in peephole optimizer has to do extra work fixing up jump targets and the line-number table, and just punts on many cases because they’re too hard to deal with.

One common "advanced question" on places like StackOverflow and python-list is "how do I dynamically create a function/method/class/whatever"? The standard answer is: first, some caveats about why you probably don't want to do that, and then an explanation of the various ways to do it when you reall

A few years ago, Cesare di Mauro created a project called WPython, a fork of CPython 2.6.4 that “brings many optimizations and refactorings”. The starting point of the project was replacing the bytecode with “wordcode”. However, there were a number of other changes on top of it.

Many languages have a for-each loop.

When the first betas for Swift came out, I was impressed by their collection design. In particular, the way it allows them to write map-style functions that are lazy (like Python 3), but still as full-featured as possible.

In a previous post, I explained in detail how lookup works in Python.

The documentation does a great job explaining how things normally get looked up, and how you can hook them.

But to understand how the hooking works, you need to go under the covers to see how that normal lookup actually happens.

When I say "Python" below, I'm mostly talking about CPython 3.5.

In Python (I'm mostly talking about CPython here, but other implementations do similar things), when you write the following:

def spam(x): return x+1 spam(3) What happens?

Really, it's not that complicated, but there's no documentation anywhere that puts it all together.

I've seen a number of people ask why, if you can have arbitrary-sized integers that do everything exactly, you can't do the same thing with floats, avoiding all the rounding problems that they keep running into.

In a recent thread on python-ideas, Stephan Sahm suggested, in effect, changing the method resolution order (MRO) from C3-linearization to a simple depth-first search a la old-school Python or C++.

Note: This post doesn't talk about Python that much, except as a point of comparison for JavaScript.

Most object-oriented languages out there, including Python, are class-based. But JavaScript is instead prototype-based.

About a year and a half ago, I wrote a blog post on the idea of adding pattern matching to Python.

I finally got around to playing with Scala semi-seriously, and I realized that they pretty much solved the same problem, in a pretty similar way to my straw man proposal, and it works great.

About a year ago, Jules Jacobs wrote a series (part 1 and part 2, with part 3 still forthcoming) on the best collections library design.

In three separate discussions on the Python mailing lists this month, people have objected to some design because it leaks something into the enclosing scope. But "leaks into the enclosing scope" isn't a real problem.

There's a lot of confusion about what the various kinds of things you can iterate over in Python. I'll attempt to collect definitions for all of the relevant terms, and provide examples, here, so I don't have to go over the same discussions in the same circles every time.

Python has a whole hierarchy of collection-related abstract types, described in the collections.abc module in the standard library. But there are two key, prototypical kinds. Iterators are one-shot, used for a single forward traversal, and usually lazy, generating each value on the fly as requested.

There are a lot of novice questions on optimizing NumPy code on StackOverflow, that make a lot of the same mistakes. I'll try to cover them all here.

What does NumPy speed up?

Let's look at some Python code that does some computation element-wise on two lists of lists.

When asyncio was first proposed, many people (not so much on python-ideas, where Guido first suggested it, but on external blogs) had the same reaction: Doing the core reactor loop in Python is going to be way too slow. Something based on libev, like gevent, is inherently going to be much faster.

Let's say you have a good idea for a change to Python.

There are hundreds of questions on StackOverflow that all ask variations of the same thing. Paraphrasing:

lst is a list of strings and numbers. I want to convert the numbers to int but leave the strings alone.

In Haskell, you can section infix operators. This is a simple form of partial evaluation. Using Python syntax, the following are equivalent:

(2*) lambda x: 2*x (*2) lambda x: x*2 (*) lambda x, y: x*y So, can we do the same in Python?

Grammar

The first form, (2*), is unambiguous.

Many people—especially people coming from Java—think that using try/except is "inelegant", or "inefficient". Or, slightly less meaninglessly, they think that "exceptions should only be for errors, not for normal flow control".

These people are not going to be happy with Python.

If you look at Python tutorials and sample code, proposals for new language features, blogs like this one, talks at PyCon, etc., you'll see spam, eggs, gouda, etc. all over the place.

Most control structures in most most programming languages, including Python, are subordinating conjunctions, like "if", "while", and "except", although "with" is a preposition, and "for" is a preposition used strangely (although not as strangely as in C…).

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

Some languages have a very strong idiomatic style—in Python, Haskell, or Swift, the same code by two different programmers is likely to look a lot more similar than in Perl, Lisp, or C++.

There's an advantage to this—and, in particular, an advantage to you sticking to those idioms.

Python doesn't have a way to clone generators.

At least for a lot of simple cases, however, it's pretty obvious what cloning them should do, and being able to do so would be handy. But for a lot of other cases, it's not at all obvious.

Every time someone has a good idea, they believe it should be in the stdlib. After all, it's useful to many people, and what's the harm? But of course there is a harm.

This confuses every Python developer the first time they see it—even if they're pretty experienced by the time they see it:

>>> t = ([], []) >>> t[0] += [1] --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <stdin> in <module>()