Long Live Dynamic Languages!

Posted on Wed 24 May 2017 in Programming • Tagged with Python, Rust, dynamic languages, dynamic typing, static typingLeave a comment

If you followed the few (or a dozen) of my recent posts, you’ve probably noticed a sizable bias in the choice of topics. The vast majority were about Rust — a native, bare metal, statically typed language with powerful compile time semantics but little in the way of runtime flexibility.

Needless to say, Rust is radically different than (almost the exact opposite of) Python, the other language that I’m covering sometimes. Considering this topical shift, it would fair to assume that I, too, have subscribed to the whole Static Typing™ trend.

But that wouldn’t be very accurate.

Don’t get me wrong. As far as fashion cycles in the software industry go, the current trend towards static/compiled languages is difficult to disparage. Strong in both hype and merit, it has given us some really innovative & promising solutions (as well as some not-so-innovative ones) that are poised to shape the future of programming for years, if not decades to come. In many ways, it is also correcting mistakes of the previous generation: excessive boilerplate, byzantine abstractions, and software bloat.

What about dynamic languages, then? Are they slowly going the way of the dodo?

Trigger warning: TypeError

Some programmers would certainly wish so.

Indeed, it’s not hard at all to find articles and opinions about dynamic languages that are, well, less than flattering.

The common argument echoed in those accounts points to supposed unsuitability of Python et al. for any large, multi-person project. The reasoning can be summed up as “good for small scripts and not much else”. Without statically checked types, the argument goes, anything bigger than a quick hack or a prototype shall inevitably become hairy and dangerous monstrosity.

And when that happens, a single typo can go unchecked and bring down the entire system!…

At the very end of this spectrum of beliefs, some pundits may eventually make the leap from languages to people. If dynamically typed languages (or “untyped” ones, as they’re often mislabeled) are letting even trivial bugs through, then obviously anyone who wants to use them is dangerously irresponsible. It must follow that all they really want is to hack up some shoddy code, yolo it over to production, and let others worry about the consequences.

Mind the gap

It’s likely unproductive to engage with someone who’s that extreme. If the rhetoric is dialed down, however, we can definitely find the edge of reason.

In my opinion, this fine line goes right through the “good in small quantities” argument. I can certainly understand the apprehension towards large projects that utilize dynamically typed languages throughout their codebases. The prospect of such a project is scary, because it contains an additional element of uncertainty. More so than with many other technologies, you ought to know what you’re doing.

Some people (and teams) do. Others, not so much.

I would therefore refine the argument so that it better reflects the strengths and weaknesses of dynamic languages. They are perfectly suited for at least the following cases:

  • anyone writing small, standalone applications or scripts
  • any project (large or small) with a well-functioning team of talented individuals

The sad reality of the software industry is the vast, gaping chasm of calamity and despair that stretches between those two scenarios.

Within lies the bulk of commercial software projects, consistently hamstrung by the usual suspects: incompetent management, unclear and shifting requirements, under- or overstaffing, ancient development practices, lack of coding standards, rampant bureaucracy, inexperienced developers, and so on.

In such an environment, it becomes nigh impossible to capitalize on the strengths of dynamic languages. Instead, the main priority is to protect from even further productivity losses, which is what bog-standard languages like Java, C#, or Go tend to be pretty good at. Rather than to move fast, the objective is to remain moving at all.

Freedom of choice

But that’s backwards”, the usual retort goes. “Static typing and compilation checks are what enables me to be productive!”

I have no doubt that most people saying this do indeed believe they’re better off programming in static languages. Regardless of what they think, however, there exists no conclusive evidence to back up such claims as a universal rule.

This is of course the perennial problem with software engineering in general, and the project management aspect of it in particular. There is very little proper research on optimal and effective approaches to it, which is why any of the so-called “best practices” are quite likely to stem from unsubstantiated hearsay.

We can lament this state of affairs, of course. But on the other hand, we can also find it liberating. In the absence of rigid prescriptions and judgments about productivity, we are free to explore, within technical limitations, what language works best for us, our team, and our projects.

Sometimes it’ll be Go, Java, Rust, or even Haskell.
A different situation may be best handled by Python, Ruby, or even JavaScript.

As the old adage goes, there is no silver bullet. We should not try to polish static typing into one.

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Arguments to Python generator functions

Posted on Tue 14 March 2017 in Code • Tagged with Python, generators, functions, arguments, closuresLeave a comment

In Python, a generator function is one that contains a yield statement inside the function body. Although this language construct has many fascinating use cases (PDF), the most common one is creating concise and readable iterators.

A typical case

Consider, for example, this simple function:

def multiples(of):
    """Yields all multiples of given integer."""
    x = of
    while True:
        yield x
        x += of

which creates an (infinite) iterator over all multiples of given integer. A sample of its output looks like this:

>>> from itertools import islice
>>> list(islice(multiples(of=5), 10))
[5, 10, 15, 20, 25, 30, 35, 40, 45, 50]

If you were to replicate in a language such as Java or Rust — neither of which supports an equivalent of yield — you’d end up writing an iterator class. Python also has them, of course:

class Multiples(object):
    """Yields all multiples of given integer."""

    def __init__(self, of):
        self.of = of
        self.current = 0

    def __iter__(self):
        return self

    def next(self):
        self.current += self.of
        return self.current

    __next__ = next  # Python 3

but they are usually not the first choice1.

It’s also pretty easy to see why: they require explicit bookkeeping of any auxiliary state between iterations. Perhaps it’s not too much to ask for a trivial walk over integers, but it can get quite tricky if we were to iterate over recursive data structures, like trees or graphs. In yield-based generators, this isn’t a problem, because the state is stored within local variables on the coroutine stack.


It’s important to remember, however, that generator functions behave differently than regular functions do, even if the surface appearance often says otherwise.

The difference I wanted to explore in this post becomes apparent when we add some argument checking to the initial example:

def multiples(of):
    """Yields all multiples of given integer."""
    if of < 0:
        raise ValueError("expected a natural number, got %r" % (of,))

    x = of
    while True:
        yield x
        x += of

With that if in place, passing a negative number shall result in an exception. Yet when we attempt to do just that, it will seem as if nothing is happening:

>>> m = multiples(-10)

And to a certain degree, this is pretty much correct. Simply calling a generator function does comparatively little, and doesn’t actually execute any of its code! Instead, we get back a generator object:

>>> m
<generator object multiples at 0x10f0ceb40>

which is essentially a built-in analogue to the Multiples iterator instance. Commonly, it is said that both generator functions and iterator classes are lazy: they only do work when we asked (i.e. iterated over).

Getting eager

Oftentimes, this is perfectly okay. The laziness of generators is in fact one of their great strengths, which is particularly evident in the immense usefulness of theitertools module.

On the other hand, however, delaying argument checks and similar operations until later may hamper debugging. The classic engineering principle of failing fast applies here very fittingly: any errors should be signaled immediately. In Python, this means raising exceptions as soon as problems are detected.

Fortunately, it is possible to reconcile the benefits of laziness with (more) defensive programming. We can make the generator functions only a little more eager, just enough to verify the correctness of their arguments.

The trick is simple. We shall extract an inner generator function and only call it after we have checked the arguments:

def multiples(of):
    """Yields all multiples of given integer."""
    if of < 0:
        raise ValueError("expected a natural number, got %r" % (of,))

    def multiples():
        x = of
        while True:
            yield x
            x += of

    return multiples()

From the caller’s point of view, nothing has changed in the typical case:

>>> multiples(10)
<generator object multiples at 0x110579190>

but if we try to make an incorrect invocation now, the problem is detected immediately:

>>> multiples(-5)
Traceback (most recent call last):
  File "<pyshell#2>", line 1, in <module>
  File "<pyshell#0>", line 4, in multiples
    raise ValueError("expected a natural number, got %r" % (of,))
ValueError: expected a natural number, got -5

Pretty neat, especially for something that requires only two lines of code!

The last (micro)optimization

Indeed, we didn’t even have to pass the arguments to the inner (generator) function, because they are already captured by the closure.

Unfortunately, this also has a slight performance cost. A captured variable (also known as a cell variable) is stored on the function object itself, so Python has to emit a different bytecode instruction (LOAD_DEREF) that involves an extra pointer dereference. Normally, this is not a problem, but in a tight generator loop it can make a difference.

We can eliminate this extra work2 by passing the parameters explicitly:

    # (snip)

    def multiples(of):
        x = of
        while True:
            yield x
            x += of

    return multiples(of)

This turns them into local variables of the inner function, replacing the LOAD_DEREF instructions with (aptly named) LOAD_FAST ones.

  1. Technically, the Multiples class is here is both an iterator (because it has the next/__next__ methods) and iterable (because it has __iter__ method that returns an iterator, which happens to be the same object). This is common feature of iterators that are not associated with any collection, like the ones defined in the built-in itertools module

  2. Note that if you engage in this kind of microoptimizations, I’d assume you have already changed your global lookup into local ones :) 

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__all__ and wild imports in Python

Posted on Mon 26 December 2016 in Code • Tagged with Python, modules, imports, testingLeave a comment

An often misunderstood piece of Python import machinery is the __all__ attribute. While it is completely optional, it’s common to see modules with the __all__ list populated explicitly:

__all__ = ['Foo', 'bar']

class Foo(object):
    # ...

def bar():
    # ...

def baz():
    # ...

Before explaining what the real purpose of __all__ is (and how it relates to the titular wild imports), let’s deconstruct some common misconceptions by highlighting what it isn’t:

  • __all__ doesn’t prevent any of the module symbols (functions, classes, etc.) from being directly imported. In our the example, the seemingly omitted baz function (which is not included in __all__), is still perfectly importable by writing from module import baz.

  • Similarly, __all__ doesn’t influence what symbols are included in the results of dir(module) or vars(module). So in the case above, a dir call would result in a ['Foo', 'bar', 'baz'] list, even though 'baz' does not occur in __all__.

In other words, the content of __all__ is more of a convention rather than a strict limitation. Regardless of what you put there, every symbol defined in your module will still be accessible from the outside.

This is a clear reflection of the common policy in Python: assume everyone is a consenting adult, and that visibility controls are not necessary. Without an explicit __all__ list, Python simply puts all of the module “public” symbols there anyway1.

The meaning of it __all__

So, what does __all__ actually effect?

This is neatly summed up in this brief StackOverflow answer. Simply speaking, its purpose is twofold:

  • It tells the readers of the source code — be it humans or automated tools — what’s the conventional public API exposed by the module.

  • It lists names to import when performing the so-called wild import: from module import *.

Because of the default content of __all__ that I mentioned earlier, the public API of a module can also be defined implicitly. Some style guides (like the Google one) are therefore relying on the public and _private naming exclusively. Nevertheless, an explicit __all__ list is still a perfectly valid option, especially considering that no approach offers any form of actual access control.

Import star

The second point, however, has some real runtime significance.

In Python, like in many other languages, it is recommended to be explicit about the exact functions and classes we’re importing. Commonly, the import statement will thus take one of the following forms:

import random
import urllib.parse
from random import randint
from logging import fatal, warning as warn
from urllib.parse import urlparse
# etc.

In each case, it’s easy to see the relevant name being imported. Regardless of the exact syntax and the possible presence of aliasing (as), it’s always the last (qualified) name in the import statement, before a newline or comma.

Contrast this with an import that ends with an asterisk:

from itertools import *

This is called a star or wild import, and it isn’t so straightforward. This is also the reason why using it is generally discouraged, except for some very specific situations.

Why? Because you cannot easily see what exact names are being imported here. For that you’d have to go to the module’s source and — you guessed it — look at the __all__ list2.

Taming the wild

Barring some less important details, the mechanics of import * could therefore be expressed in the following Python (pseudo)code:

import module as __temp
for __name in module:
    globals()[name] = getattr(__temp, __name)
del __temp
del __name

One interesting case to consider is what happens when __all__ contains a wrong name.

What if one of the strings there doesn’t correspond to any name within the module?…

# foo.py
__all__ = ['Foo']

def bar():

>>> import foo
>>> from foo import *
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'module' object has no attribute 'Foo'

Quite predictably, import * blows up.
Notice, however, that regular import still works.

All in all (ahem), this hints at a cute little trick which is also very self-evident:

__all__ = ['DO_NOT_WILD_IMPORT']

Put this in a Python module, and no one will be able to import * from it!
Much more effective than any lint warning ;-)

Test __all__ the things

Jokes aside, this phenomenon (__all__ with an out-of-place name in it) can also backfire. Especially when reexporting, it’s relatively easy to introduce stray 'name' into __all__: one which doesn’t correspond to any name that’s actually present in the namespace.

If we commit such a mishap, we are inadvertently lying about the public API of our package. What’s worse is that this mistake can propagate through documentation generators, and ultimately mislead our users.

While some linters may be able to catch this, a simple test like this one:

def test_all(self):
    """Test that __all__ contains only names that are actually exported."""
    import yourpackage

    missing = set(n for n in yourpackage.__all__
                  if getattr(yourpackage, n, None) is None)
        missing, msg="__all__ contains unresolved names: %s" % (
            ", ".join(missing),))

is a quick & easy way to ensure this never happens.

  1. Public” symbols have names that don’t begin with underscore (_). Of course, “non-public” ones are still accessible but are treated as implicitly unstable & discouraged. 

  2. Or check what symbols there don’t have a leading underscore. 

Continue reading

The brave “new” world of Python 3

Posted on Mon 15 August 2016 in Code • Tagged with Python, Python 3, Unicode, lazy evaluation, iterablesLeave a comment

I’ll blurt it straight up: I’m not a big fan of Python 3.

For a long time, I resisted the appeal of various incremental improvements that early 3.x releases offered. And the world agreed with me: a mere two years ago, Python 3 wasn’t even a blip on the PyPI radar.

Lately, however, things seem to be picking up some steam.

As if to compensate for years of “good enough”, Python 3 development team has given in to a steadily accelerating feature creep. Sure, some of it results in bad ideas (or even ideas you’d hope are jokes), but it nevertheless causes an increasingly wide functional gap between the 2.x and 3.x series.

Starting from around Python 3.5, this gap becomes really noticeable, even when partially bridged with many excellent backports. The ecosystem support is also mostly there, at least insofar as “not breaking horribly when a package is used in Python 3”.

And then, of course, there is the 2.7 EoL date looming ever closer.

Given all those portents, even old curmudg… ahem… seasoned developers cannot really ignore Python 3 anymore. For better or for worse, 3.x is how Python will look like in the coming years and decades. Might as well prepare for it.

In this post, I will discuss some important issues one should be aware of before trying to switch from Python 2 to 3. I won’t be talking about all the minute changes and additions, but cover the more significant, broader concepts that mark the divide between the 2.x and 3.x generations.

The two concepts I’ll be mentioning here are Unicode (obviously) and lazy vs. eager computation.

Unicode handling

You have probably heard it before. Python 3 was going to solve your Unicode problems once and for all. You haven’t believed it, of course, like you wouldn’t believe in any other silver bullet.

Still, it may be rather surprising to learn that in Python 3, you’ll actually see much more Unicode-related errors.

And strange as it may sound, it is a good thing.

In any case, either version of Python gets the most important thing about Unicode right. They both distinguish, at the type level, between strings (of Unicode codepoints) and their encodings (sequences of bytes). The type that holds the latter is called bytes in both versions, while strings are stored within the str type in Python 3 and unicode in Python 2.

It is from this crucial distinction — or rather, failing to account for it — where all the dreaded Unicode errors ultimately stem.

But where Python 2 does poorly is in the choice of defaults. You probably know all too well that bytes there is just an alias for str. That str is a fully functional string type, even though it can only contain ASCII characters. Moreover, it is also the default: quoted string literals, for example, will be of this type unless specially marked.

This poor choice of defaults is the primary source of latent Unicode bugs in Python 2 programs.

What Python 3 does here is to help expose those bugs sooner. If you already deal with Unicode correctly in your programs — maybe because you watched this excellent talk by Ned Batchelder — your main benefit will be not having to write that u"" quotes anymore. Otherwise, it’ll force you to consider the issue from the very beginning, rather than letting you write “working” programs that crash the moment they have to process some non-ASCII input.

Laziness by default

The second major change that Python 3 brings is of similar nature. It is also a change of defaults, but the impetus for it is much less evident.

What’s different in Python 3 is that many built-in functions and methods which used to return lists are now giving out bespoke objects that only mostly behave like lists. Included in these are functions like map or filter, as well as common dictionary methods such as keys or values.

This change is usually presented as removal of unnecessary cruft:

  • itertools.ifilter is now just filter
  • xrange is now just range
  • dict.iteritems is now just dict.items

and so on.

In some cases, this is exactly what happens. For example, there is virtually no downside to the new implementation of range, especially considering the way it is used most often.

But not every built-in managed to preserve all the functionality of lists. Indeed, many have downgraded their API guaratees to those of mere generators, i.e. the most simplistic and limited flavor of Python iterables. Working with them is trickier and more error-prone than with lists, which is due to various pitfalls that generators expose us to.

Navigating around those gotchas used to be something that Python code had to opt-in to, by explicitly importing the itertools module and using its functions in place of the built-ins. What you could gain in return was increased performance, and a lesser memory footprint. All those benefits came from making the computations lazy and refraining from storage of the intermediate results.

In Python 3, however, laziness is preordained. Even if we don’t need or care about the aforementioned perks, we have to devise some way of dealing with the pervasive generators.

One option is to embrace lazy evaluation fully, and adapt to handling unspecified iterables throughout our code bases.
The risk is an increased frequency of bugs stemming from generator misuse — including a common mistake of trying to iterate over lazy foos the second time, deeper down a long function, after it’s been already exhausted.

The alternative is to engage in a lot of “defensive listing”: wrapping of unknown (or known-but-lazy) iterables in list() calls in order to “sanitize” them for later (re)use.
Examples include immediate listification of a generator object:

primes = list(filter(is_prime, range(1000)))

or preemptive conversion of an incoming iterable argument:

def do_something(foos):
    foos = list(foos)
    # ...the rest of a long function...

Even if you choose the first path, and somehow use lazy generators everywhere, conversions are still required at the serialization boundaries:

d = {'foo': 42}
json.dumps({'keys': d.keys()})  # TypeError: dict_keys(['foo']) is not JSON serializable
json.dumps({'keys': list(d.keys())})  # works

At least in this case, the lazy iterable will vocally fail with an exception, rather than silently doing nothing (in case of repeated iteration) or always posing as truthy even when it’s empty (in if iterable: checks).

from __future__ import doubts

So, here they are: the highlights of Python 3. If you are disappointed they all turned out to be mixed blessings, don’t worry: you are in a good company.

The truth is that Python 3 is more finnicky, less forgiving, and much less beginner-friendly than its predecessor. Its various superficial simplifications are almost squarely balanced by many new concerns that are thrust upon an unsuspecting programmer from the very beginning.

In one possible view, this is simply a sign that the language has matured. Perhaps it’s not a coincidence that almost exactly 18 years has passed between the first public version of Python (0.9) and the release of Python 3.0. By no conceivable means it is a toy language anymore, and it’s adequately equipped to tackle challenges presented by the computing world of today.

But on the other hand, it’s clear something is being gradually lost in the process.

It’s becoming harder to claim the language favors simplicity over complexity.
It is no longer so easy to pick which way is the obvious way to do it.
It is increasingly often that ugly replaces beautiful and nested replaces flat.

Little by little, Python itself is becoming less and less pythonic. The pace isn’t breakneck, but it’s definitely noticeable. But who knows? Maybe after two decades, a wholesale redefinition of the language’s core principles really is in order.

…Well, certainly that’s necessary if some of the latest ideas are about to get in!

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str.startswith() with tuple argument

Posted on Tue 28 June 2016 in Code • Tagged with Python, strings, tuplesLeave a comment

Here’s a little known trick that’s applicable to Python’s startswith and endswith methods of str (and unicode).

Suppose you’re checking whether a string starts with some prefix:

if s.startswith('http://'):
    # totally an URL

You eventually add more possible prefixes (or suffixes) to your condition:

if s.startswith('http://') or s.startswith('https://'):
    # ...

Later on you notice the repetition and refactor it into something like this:

SCHEMES = ['http://', 'https://', 'ftp://', 'git://']
if any(s.startswith(p) for p in SCHEMES):
    # ...

or if you’re feeling extra functional:

if any(map(s.startswith, SCHEMES)):
    # ...

Turns out, however, that startswith (and endswith) support this use case natively. Rather than passing just a single string as the argument, you can provide a tuple of strings instead:

SCHEMES = ('http://', 'https://', 'ftp://', 'git://')
if s.startswith(SCHEMES):
    # ...

Either method will then check the original string against every element of the passed tuple. Both will only return True if at least one of the strings is recognized as prefix/suffix. As you can see, that’s exactly what we would previously do with any.

Somewhat surprisingly, however, the feature only works for actual tuples. Trying to pass a seemingly equivalent iterable — a list or set, for example — will be met with interpreter’s refusal:

>>> is_jpeg = filename.endswith(['.jpg', '.jpeg'])
TypeError: endswith first arg must be str, unicode, or tuple, not list

If you dig into it, there doesn’t seem to be a compelling reason for this behavior. The relevant feature request talks about consistency with the built-in isinstance function, but it’s quite difficult to see how those two are related.

In any case, this can be worked around without much difficulty:

PROTOCOLS = ('http', 'https', 'ftp', 'git')
if s.startswith(tuple(p + '://' for p in PROTOCOLS)):
    # ...

though ideally, you’d want to pack the prefixes in a tuple to begin with.

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…or lambda?

Posted on Mon 20 June 2016 in Code • Tagged with Python, syntax, lambda, operatorsLeave a comment

a.k.a. Curious Facts about Python Syntax

In Python 3.3, a new method has been added to the str type: casefold. Its purpose is to return a “sanitized” version of the string that’s suitable for case-insensitive comparison. For older versions of Python, an alternative way that’s mostly compatible is to use the str.lower method, which simply changes all letters in the string to lowercase.

Syntax is hard

Easy enough for a compatibility shim, right? That’s exactly what I thought when I came up with this:

casefold = getattr(str, 'casefold', None) or lambda s: s.lower()

Let’s ignore for a moment the fact that for a correct handling of unicode objects in Python 2, a much more sophisticated approach is necessary. What’s rather more pertinent is that this simple code doesn’t parse:

  File "foo.py", line 42
    getattr(str, 'casefold', None) or lambda s: s.lower()
SyntaxError: invalid syntax

It’s not very often that you would produce a SyntaxError with code that looks perfectly valid to most pythonistas. The last time I had it happen, the explanation was rather surprising and not exactly trivial to come by.

Fortunately, there is always one place where we can definitively resolve any syntactic confusion. That place is the full grammar specification of the Python language.

It may be a little intimidating at first, especially if you’re not familiar with the ENBF notation it uses. All the Python’s language constructs are there, though, so the SyntaxError from above should be traceable to a some rule of the grammar1.

The culprit

And indeed, the offending bit is right here:

or_test: and_test ('or' and_test)*
and_test: not_test ('and' not_test)*

It says, essentially, that Python defines the or expression (or_test) as a sequence of and expressions (and_test). If you follow the syntax definition further, however, you will notice that and_test expands to comparisons (a < b, etc.), arithmetic expressions (x + y, etc.), list & dict constructors ([foo, bar], etc.), and finally to atoms such as literal strings and numbers.

What you won’t see along the way are lambda definitions:

lambdef: 'lambda' [varargslist] ':' test

In fact, the branch to allow them is directly above the or_test:

test: or_test ['if' or_test 'else' test] | lambdef

As you can see, the rule puts lambdas at the same syntactical level as conditional expressions (x if a else b), which is very high up. The only thing you can do with a lambda to make a larger expression is to add a yield keyword before it2, or follow it with a comma to create a tuple3.

You cannot, however, pass it as an argument to a binary operator, even if it otherwise makes sense and even looks unambiguous. This is also why the nonsensical expressions such as this one:

1 + lambda: None

will fail not with TypeError, but also with SyntaxError, as they won’t even be evaluated.

More parentheses

Savvy readers may have noticed that this phenomenon is very much reminiscent of the issue of operator precedence.

Indeed, in Python and in many other languages it is the grammar that ultimately specifies the order of operations. It does so simply by defining how expressions can be constructed.

Addition, for example, will be of lower priority than multiplication simply because a sum is said to comprise of terms that are products:

arith_expr: term (('+'|'-') term)*
term: factor (('*'|'/'|'%'|'//') factor)*

This makes operator precedence a syntactic feature, and its resolution is baked into the language parser and handled implicitly4.

We know, however, that precedence can be worked around where necessary by enclosing the operator and its arguments in a pair of parenthesis. On the syntax level, this means creating an entirely new, top-level expression:

atom: '(' [yield_expr|testlist_comp] ')' |  # parenthesized expression
       '[' [listmaker] ']' |
       '{' [dictorsetmaker] '}' |
       '`' testlist1 '`' |

There, it is again possible to use even the highest-level constructs, including also the silly stuff such as trying to add a number to a function:

1 + (lambda: None)

This expression will now parse correctly, and produce TypeError as expected.

In the end, the resolution of our initial dilemma is therefore rather simple:

casefold = getattr(str, 'casefold', None) or (lambda s: s.lower())

  1. Such rules are sometimes called productions of the grammar, a term from computational linguistics. 

  2. Yes, yield foo is an expression. Its result is the value sent to the generator by outer code via the send method. Since most generators are used as iterables, typically no values are passed this way so the result of a yield expression is None

  3. There are also a legacy corner cases of lambdas in list/dict/etc. comprehensions, but those only apply under Python 2.x. 

  4. This saying, there are languages where the order is resolved at later stage, after the expressions have already been parsed. They usually allow the programmer to change the precedence of their own operators, as it’s the case in Haskell

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Please don’t use Click

Posted on Fri 20 May 2016 in Programming • Tagged with Python, CLI, UI, ClickLeave a comment

…not for standalone programs anyway.

Chances are, you have written some command line programs in Python. This is quite probable even if you normally code in some other language. And if you have, it is not unlikely that you needed to parse the argv of your program at one point or another.

There are plenty of options here, both in the standard library as well as among third party packages. One does stand out, however, and it’s mostly for how it is often overused. I’m talking about Click here.

If you wanted to use it in your next Python program, I hereby urge you to reconsider.

What’s the fuss?

click_ The somewhat bizarrely named Click library is described as a “package for creating beautiful command line interfaces”. Its main trick is the ability to create subcommands by adorning Python functions with the @click.command() decorator1. It then makes them coalesce into an argument parser, equipped with the necessary dispatching logic.

This idea isn’t new, of course. Prior art goes back at least seven years to the now-abandoned opster package. Click, however, was the first one of its kind to garner noticeable popularity, which is easily attributed to whom it’s been authored by.

So while my arguments against using this kind of CLI framework would apply to any package implementing the paradigm, it just happens that Click is currently its most prominent example. Purely for the sake of convenience, I will therefore refer to it as if it was interchangeable with the whole concept. Because why not? Whatever you may say about the library’s name, it’s hard to imagine a more concise moniker than a simple Click.

What’s wrong, then, with the way Click handles command line interfaces?

CLI: Little Interfaces

It’s how it encourages to treat them as an accidental afterthought rather than a deliberate design decision.

For applications invoked repeatedly from a terminal, their command line arguments and flags are the primary means of user interaction2. It is how users communicate their intent to perform an action; provide the neccessary input data to carry it throgh; decide how they want to receive the output; and control many other aspects of the programs execution. Absent graphical components and widgets, the command line is virtually the only way to interact with a terminal program.

In other words, it is the UI.

And how important the UI is for any application? It seems to be important enough that entire fields of study are devoted to reducing friction of human-computer interaction. In many projects, the emphasis on user interface design is on par with that of actual software engineering.
Like everything, of course, it is susceptible to trends and fads (such as the recent “mobile/responsive everything!” craze). But its significance remains undiminished. Quite the opposite: in the age of ubiquitous computing, user interfaces are probably more important than ever.

Yes, this includes CLI. One of the main reasons we turn to the command line are speed and efficacy. Common tasks must utilize short and convenient syntax that is quick to integrate into user’s muscle memory. Others should not only be possible, but discoverable and accessible without going through reams of man pages.

Any terminal program intended for frequent use by humans should therefore strive to excel in those two qualities. But except for the simplest of cases, it won’t happen by itself. Designing an efficient CLI for any non-trivial application is a challenging and demanding task.

It doesn’t click

With Click, however, we’re encouraged to just wing it.

Click tells us to slap some decorators on our top-level functions and call it a day. Sure, you can dig deep enough and uncover the underlying layers of abstraction that may eventually allow you do things for which argparse has a first-class support.

By default, however, Click shoehorns your programs into predefined patterns that, incidentally, mirror those of some least intuitive command-line tools in existence.

Indeed, the whole idea of subdiving your program into several distinct is already suspect, for it appears at odds with the fundamental Unix philosophy of doing one thing well. While it is occasionally justified, it shouldn’t be the first thing that comes to your mind. But that’s completely at odds with the Click’s approach, where not ending up with multiple distinct commands is something you have to consciously avoid.

…though it sometimes might

So, what am I suggesting you use instead libraries such as Click?… Nothing outrageous, really.

If you care about your command line interface, consider just using the argparse module. Yes, it will force you to create parser objects, add arguments & flags to it, and in general pay some attention to the whole business. When it comes to UI, it’s always good to make it an explicit concern, maybe even sufficient to warrant its own module.

Alternatively, the docopt library provides another take on the UI-first approach to CLI, though it is more limited in its capabilities3.

Finally, I’m not advocating to ditch Click in all scenarios. There’s plenty of situations when we’re interested in getting any CLI up and running, and not so much in making the most efficient and intuitive interface possible. The prime example is any kind of automation scripts that are ancillary to some bigger project, like manage.py is in Django4. The Python ecosystem doesn’t really have dedicated task runners that are as featureful as Grunt or Gulp, and that makes Click a viable and compelling option5.

But for standalone programs whose CLI is the main interface? Yeah, not really.

  1. Oddly enough, that pair of parentheses seems to be mandatory. 

  2. Environment variables and config files deserve a honorary mention, of course. But those are usually derivatives of the command line arguments, containing e.g. the default values for flags. 

  3. Click’s own documentation actually describes quite nicely how theirs and docopt’s philosophies differ in a way that’s consistent with this article. 

  4. Incidentally, this appears to be a major motivation behind creating Click in the first place: to support web applications built upon on the Flask framework, and possibly obviate the need for extensions such as Flask-Script

  5. This saying, there are some task runners which offer similar experience, like Invoke

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Mock.configure_mock fix for Python

Posted on Sat 07 May 2016 in Code • Tagged with Python, mock, patchingLeave a comment

Python’s mocking library is rather uncomplicated. Most of what it does is creating mock objects: veritable sponges that absorb every interaction with any code that we pass them to.

This simplicity is also surfaced in the API, especially in the main part of it — the mock.Mock constructor:

some_mock = mock.Mock(url='http://example.com')
assert some_mock.url == 'http://example.com'

Any arguments that we pass there become attributes on the resulting mock object. This is really useful when patching, because it allows us to completely specify the replacement object within a @mock.patch decorator:

@patch.object(requests, 'get', new=Mock(return_value=Mock(status_code=400)))
def test_404(self):

You have to keep in mind, however, that the mock.Mock class also has some constructor arguments of its own. For this reason, there exists some potential for name collision: some of the Mocks own arguments may have the same names as the attributes we’d like to set on the mock object:

some_mock = mock.Mock(name="John Doe")  # doesn't set the `name` attribute
assert some_mock.name == "John Doe"  # blows up!

Here, the name argument is inherent to the Mock class. Its constructor will interpret it in a special way, and so it won’t set a name attribute on the resulting mock. Other possible culprits include the spec and wraps parameters, both of which have relatively common names that we may want to use as object attributes1.

Collision avoidance

It’s trivial to fix the issue, of course:

some_mock = mock.Mock()
some_mock.name = "John Doe"
assert sock_mock.name == "John Doe"

but this approach has a downside. Creating and configuring a mock is no longer a single expression, which means we cannot use it with patchers as easily as before:

@patch.object(foo, 'bar')
def test_something(self, mock_bar):
    mock_bar.name = "John Doe"
    # (...rest of the test...)

We can either configure the mock after patching, like above, or perhaps introduce some utility functions to be called inside the @patch decorator.

The almost-there method

In any case, this is somewhat disappointing. And it is even more so when we discover that there is a method called configure_mock which looks like it was designed to solve this very issue. Its arguments are always interpreted as attributes of the mock: it has no “special” or “reserved” names. Indeed, this method is what allows us to actually write the mock setup as a single expression:

some_mock.configure_mock(name="John Doe")

Problem is, this expression returns None.

Yes, configure_mock returns nothing.
Or in other words, it doesn’t return anything.
In fact, it has no return statement whatsoever.

Most importantly, it doesn’t have the return self line that’d enable us to write this:

some_mock = mock.Mock().configure_mock(name="John Doe")

Well, that is quite a let-down.

Fixing it

But hey, this is Python! Shortcomings like that don’t necessarily mean we have to fork whole libraries. Let’s just add the missing return, shall we?

from mock import Mock as _Mock

class Mock(_Mock):
    def configure_mock(self, **kwargs):
        super(Mock, self).configure_mock(**kwargs)
        return self  # <-- there!

Whew, that was quick!

…Alright, that’s actually the whole fix, but it’s close. To complete it, we need to apply the same treatment to three more Mock classes: MagicMock, NonCallableMock, and NonCallableMagicMock.

A complete solution can be seen in this gist.

  1. Collision may also occur with mock.patch constructs. The most likely offender there is probably the new parameter

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