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tribaal

The more frequent and dangerous pitfalls are, in my humble opinion:

- Bare except: statements (that catches everything, even Ctrl-C)

- Mutables as default function/method arguments

- Wildcard imports!

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Couldn't agree more! One of my all time new python interview questions gets a surprisingly large number of developers.

Given a function like:

    def append_one(l=[]):

        l.append(1)

        return l

What does this return each time?

    >>> append_one()

    >>> append_one()

    >>> append_one()

The l (lowercase L) and the 1 (one) look really similar. Could that be the cause of some confusion? Of course, the function name helps, but most developers have learned not to trust function names to be an accurate description of what the function does, especially in tricky interview questions.

Still, I'd change this to something like:

    def append_five(l=[]):

        l.append(5)

        return l

It tests the same thing (knowledge of how default parameters work), but without the confounding problem of similar-looking characters. Of course, syntax highlighting would help the applicant out.

All of that being said, I still don't doubt that many developers don't know what they should about default parameters.

I'm not a Python expert, but iirc from various blog posts the "l" variable does not get reset between function calls which will cause undesired behavior. So calling the function 3 times without argument would produce a list of size 1,2, and 3 with the third call rather than 3 lists of size 1. Can any Python guru's confirm?

Yes that is correct. The default value gets created when the function is interpreted ("compiled").

> The default value gets created when the function is interpreted ("compiled").

No. The default value gets "created" (the expression is evaluated and stored) when the def statement is executed. Take the following example:

  In [1]: def foo():
     ...:     def append_five(l=[]):
     ...:         l.append(5)
     ...:         return l
     ...:     return append_five
     ...:

  In [2]: a = foo()

  In [3]: b = foo()

  In [4]: a()
  Out[4]: [5]

  In [5]: b()
  Out[5]: [5]

  In [6]: _4 is _5
  Out[6]: False

We only wrote one function definition, but multiple lists are created. (They are created when the "def append_five" definition executes, during the execution of foo.)

I thought that was what he meant. Is there any sharp distinction between "interpreting" and "evaluating" in python that I am unaware of? I've always used the words more or less interchangeably. But now that I think about it that might be a little naive since I have no idea how it works under the hood

The parent wrote "compiled", which is certainly more incorrect than either "interpreted" or "evaluated."

You could say that interpreting is first parsing and second executing/evaluating. The parser tokenizes and does a small amount of optimization such as ignoring unassigned values.

The key is object mutability. A list type is mutable and a tuple type is immutable.

If the candidate correctly deduces what will happen, I'll ask them to write a bug-free version, which looks like one of the below:

def append_one(var=None):

    var = var or []

    var.append(1)

    return var

def append_one(var=None):

    if var is None:

        var = []

    var.append(1)

    return var

Mutability is a very subtle but very important concept to understand in python. Everyone who uses python for non-trivial code should know it well: https://docs.python.org/2/reference/datamodel.html

> The key is object mutability. A list type is mutable and a tuple type is immutable.

I don't think the question has much to do with mutability, it isn't surprising to me nor would I imagine most programmers that a list is mutable, that's very common.

The surprising part of this question is that the default value of 'l' continues to exist outside the lexical scope of the function, the expected behavior is that the value of 'l' is initialized at function call time and is garbage collected after each call. As it sits, using default values in python is sort of like defining a global that only has a named reference inside the function block, which is very strange.

It has something to do with mutability, because if an object is immutable, the behavior of Python matches what the naive developer expects. It's only mutable objects that break those expectations.

Don't even get into unexpected behavior in classes:

    In [1]: class A(object):
       ...:     l = []
       ...:

    In [2]: a, b = A(), A()

    In [3]: a.l.append("Something")

    In [4]: a.l
    Out[4]: ['Something']

    In [5]: b.l
    Out[5]: ['Something']

    In [6]: class B(object0:
       ...:
    KeyboardInterrupt

    In [6]: class B(object):
       ...:     l = None
       ...:     def __init__(self):
       ...:         self.l = []
       ...:

    In [7]: c, d = B(), B()

    In [8]: c.l.append("Something")

    In [9]: c.l, d.l
    Out[9]: (['Something'], [])

The other scoping issue in python that always struck me as strange is that loop variables aren't scoped to the loop, they continue to exist after the loop completes. I can see the logic for this feature even if I don't agree with it, but what I really don't get is that the loop variables are not defined if you iterate over something that is empty:

   >>> for item in [1]:
   ...   print item
   1
   >>> item
   1

   >>> for i in []:
   ...   print i
   
   >>> i
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   NameError: name 'i' is not defined

I would expect i == None. That oddity makes it dangerous to use the feature unless you're really careful (e.g. using a for - else construct).

Then there's for-else:

    In [1]: for i in []:
       ...:     pass
       ...: else:
       ...:     print 'Else!'
       ...:
    Else!

    In [2]: for i in []:
       ...:     break
       ...: else:
       ...:    print 'Else!'
       ...:
    Else!

    In [3]: for i in range(2):
       ...:     break
       ...: else:
       ...:     print 'Else!'
       ...:

    In [4]: for i in range(2):
       ...:     pass
       ...: else:
       ...:     print 'Else!'
       ...:
    Else!

The syntax could be interpreted as:

  if len(l) == 0:
    print "Else!"
  else:
    for i in l:
      pass

The "catch cases where a `break` is triggered" case isn't common enough for this syntax feature to be encountered very often, leading to confusion when people come across it (though at least it's not a bug where a common use-case has weird behavior to new-comers).

> but what I really don't get is that the loop variables are not defined if you iterate over something that is empty

if you conceptualize how a for-loop has to work as a while-loop using Python's iterator protocol (which is the only way the iterator protocol itself makes sense), it seems pretty intuitive.

That is, this:

  for item in items:
      ...1
  else:
      ...2

becomes, approximately:

  try:
      while True:
          __hidden_iter = items.iter()
          try: 
              item = __hidden_iter.next()
          except StopIteration:
              raise __NormalLoopExit
          ...1
  except __NormalLoopExit:
      ...2

If you have an empty loop, the first assignment doesn't complete (instead raising StopIteration in evaluating the right side, which raises the notional exception __NormalLoopExit, which invokes the else: clause, if any) so the variable never gets around to being created.

I'm sorry, but I don't understand why you would think this as unexpected behaviour? For the class A, the list l is a class-level attribute, hence it can be referred via either a or b objects, but for class B, after initialisation, l is an object attribute, so it is different for both c and d.

It's not the concept that can be confusing, it's the syntax python chose.

In most of the languages I'm familiar with, there are very clear syntax differences when working with class attributes. For example, in many languages class attributes have to be accessed via the class name instead of from an instance of the class making it clear to the programmer they are working with a class attribute, e.g. MyClass.myClassVariable not myInstance.myClassVariable. Additionally, the way you define class attributes in python is the way you define instance attributes in many languages, which just adds to the confusion. e.g. in Java or C# you can define class variables directly in the class body, but an explicit 'static' keyword is needed, undecorated definitions are assumed to be instance variables.

Finally, I think the definition of class B above is a little more nuanced, class B has both a class attribute named l AND an instance attribute named l.

B.l == None and B().l == []

Ah, gotcha!

It's been a while since I've done major OOP coding in any language other than Python, so I'm a little rusty. The issues you raise are perfectly legitimate and would be understandably confusing to newcomers to the language. :)

> if an object is immutable, the behavior of Python matches what the naive developer expects

If the object was immutable then append wouldn't work. That's hardly matching expectations.

Most immutable objects don't have methods that would mutate the value, but fail because the object is immutable...

I guess the clarification to what I was saying is that, in the simple case (integers, strings, None) the objects are immutable. It's only getting into cases where the value of the object itself is mutable, that you run into issues. If all objects (or all objects 'allowed' as default values) were immutable, then this behavior would not trigger.

So saying that mutability has nothing to do with it isn't entirely true. It's the immutability of the types of values used in most simple cases that hides this issue from developers until they run into a more complex case.

Read my post that has the "correct answers" which show you how to do it. The key is setting the default to None and then doing something like:

if val is None: val = []

or the more idiomatic python way:

    val = val or []

I'm well aware of that way to do it, but it doesn't excuse a different way being unintuitive.

I believe the former is more idiomatic, but I don't have a reference.

You want to explicitly check against `None` so that you're not overwriting all falsey values of `val` - even though you should generally try to enforce argument types, your second example would cause unexpected behavior in some cases, particularly those that have non-falsey 'default' assignments

Why? You would just get a new list back.

I can see why you might think so, but remember the Zen of Python is to have only one obvious way to do something.

    >>> [1,2,3] + [4,5]
    [1, 2, 3, 4, 5]

Thus appending should do something different than addition.

    >>> x = [1,2,3]
    >>> x.append([4,5])
    >>> x
    [1, 2, 3, [4, 5]]

In Python everything is an object, including a function. The default value isn't a global, it belongs to the function.

I wonder if people who weren't exposed to languages which work differently ala C++ would be as surprised?

I'm not a Python dev, but I've been meaning to learn for a while. So this is really interesting stuff. A few questions, if you don't mind.

I understand mutability and immutability in other languages (and I gave your link a quick read to make sure there weren't any weird Python-specific rules), so I understand how the list can change and still be the same object, but a tuple or string would not. But why does that mean that the default parameter object remains in existence throughout all calls, instead of being recreated each time it is called?

Is there a reason for this being the default behavior? It seems like the majority of the time you would want to use a default parameter, you'd want it to behave like your bug-free examples.

Think of the default parameter values as arguments to the initializer for the function object. If you passed a list into the constructor of a class, you wouldn't be surprised that if you modified the list outside the class that it would modify the same list inside the class.

While that explains how it works, I actually completely agree with you. This is surprising behavior and, in a language that prides itself on not being surprising, seems, well, surprising.

I have to wonder if performance isn't the big reason for it. If your default is [], it isn't a big deal to re-evaluate, but if your default is get_default_cities_from_slow_web_service(), having that re-evaluated on every function call would be catastrophic. Given the choice between two negatives, the choice they made is probably reasonable.

You pretty much nailed it right there.

Before I ever ask this question (I do a lot of tech interviews sadly) I always ask the candidate about object mutability vs immutability. Almost everyone knows the textbook answer, and only a few know the actual implications of it. This tests which they know :)

Default kwargs of a function are defined at function definition. However, they are only in scope, for the scope of said function. It is a weird but important subtle difference.

Since append doesn't return a value, how about:

  def append_one(var=None):
      return (var or []) + [1]

Would this take longer and/or use more storage for long lists as vars?

When you use + on two lists, a new list is created, and elements from both are copied into the new one. Whereas the append operation modifies the list, and simply adds a value. Keep in mind that a python "list" is really like a C++ vector, so while sometimes append operation sometimes allocates a new array, and copies all the values, in general is O(1). The add operation is O(n).

And besides all that, there is nothing wrong with doing an append on one line, and returning the variable on the next. It's clear and readable.

I like this, very elegant actually.

I think only the second is truly bug-free. The first only does what the user expects if they pass in a non-empty list:

  my_list = []
  append_one(my_list)
  # my_list didn't get anything appended to it

This shows up another subtle trap related to the "truthiness" (or falsiness in this case) of things like the empty list.

I'd argue that the key difference from other languages is (re)assignment rather than (im)mutability.

Yes, that's correct. The default value is only interpreted once, when the `def` statement is called. After that point, it's completely mutable. You have to see Python functions as objects and default parameter values as object variables.

The problem is not the existence of the object variables, but that they are in such an unfortunate place. The rest of the parameter list is declaring fresh local variables. It's inconsistent that the left side of the equals sign is per-invocation, and the right side is per-def.

The expression presented in the parameter list is only evaluated once, and that is when the method is defined. The confusion is that people assume the expression is evaluated every time the method is called.

The confusion is that people assume the expression is evaluated every time the method is called.

Because that's how it works in a lot of other languages, such as Ruby and Javascript.

I doubt that's the only reason. I fell for it myself at first without ever seeing a line of Ruby. It initially feels intuitive, and that's why I think most fall for it.

I think it's because the arguments are bound when the function is called. It's just natural that you'd expect the default values to also be bound at the same time.

Not the op, but I'd accept the confusion response of: [[]] [[],[]] [[],[],[]]

because the behavior is the same, whether or not they misread an 'l' as a 1.

Python doesn't seem to agree with you :^)

  In [1]: a = []

  In [2]: a.append(a)

  In [3]: a
  Out[3]: [[...]]

  In [4]: a[0]
  Out[4]: [[...]]

  In [5]: a[0][0]
  Out[5]: [[...]]

  In [6]: a[0][0][0]
  Out[6]: [[...]]

  In [7]: a[0][0][0][0]
  Out[7]: [[...]]

  In [8]: a.append(a)

  In [9]: a
  Out[9]: [[...], [...]]

  In [10]: a[0][1][0] is a
  Out[10]: True

  In [11]: id(a)
  Out[11]: 4547140064

  In [12]: id(a[0][1][0])
  Out[12]: 4547140064

Yeah, the whole infinite loop thing. Wasn't fully thinking when I wrote my reply. Good catch.

Wow, that is really ugly semantics. Here are some notes of mine on how hard R works to avoid exposing this sort of aliasing/mutability issue to the user: http://www.win-vector.com/blog/2014/04/you-dont-need-to-unde...

Yeah i really don't understand why this is just assumed to be a common 'gotcha' to be recognized and avoided by every competent python programmer. What exactly does the python spec specify as the desired behavior here? If you have this 'broken stair' that everyone should just know to step over, shouldn't somebody actually fix the stair!?

I know python is not unique in having warts like this, but it's pretty b.s. in general that unexpected behavior is just thought to be okay, especially in a language meant to be very accessible, and most especially since it's being used as a perfectly valid metric for disqualifying new python programmers from employment.

It's not broken when you understand that functions are objects and default parameters are just members of those objects. Each time the function is executed you get local vars that point to these object members. If one is a mutable type, any changes you make to it will then obviously persist.

It leaves me wondering, are the parameters also scoped to the class(seeing as they're declared at the same time)? Wouldn't this cause an issue with concurrent access to the function?

In Python there's no such thing, because GIL. Maybe in JPython.

At what level would you test an interviewee with this kind of question: Python guru, Python expert, Python ninja, Python rockstar, or merely "is familiar with Python"? Your example is a very common gotcha that has been covered ad nauseam, but IMO it's still the kind of bug that would be caught immediately in code review and is very easily fixed.

I thought we were past using trick questions like this anyways! Since, you know, it would be easy for an experienced yet anxious programmer to get tripped up on this, but someone who just browsed "python interview questions 101" to breeze on through. Also it selects against experienced multi-language developers, since language-specific quirks like this are not generally useful information to keep front-loaded, but are trivial to become re-familiar with in a work environment, or even gasp learn for the first time from a co-worker or helpful article.

If the industry as a whole cared about evidence-based, non-superstitious, non-monoculture-reinforcing hiring practices, we'd realize that tripping people up and judging programming capability based on minutia is as unfair as it is self-defeating.

I don't use this as a trick question. I ask them to describe mutable vs immutable objects and gotchas. Then I write this function and ask them to describe in excruciating detail what it does, why, and how.

It is simply a easy way to gauge a candidate's proficiency with the language. It also helps if they know that this is a problem. You'd be shocked to know a lot of people on the market for jobs writing python don't get this question correct, but the smart ones often do when talking through it even if they didn't originally.

I think the idea is to see whether the interviewee is a kind of person who always googles and reads on "gotchas of language X" whenever he/she learns X.

I would caution you not to interview on things you would not be happy to see in your code base.

In my experence your much better off with people that look at odd syntax and say, "I don't know what that does" vs those who do.

Well, this is pulled from a list of common python errors.

Using the default value in some capacity isn't that uncommon... Though maybe you were speaking to a more general case? for example, decoding a an obfuscated C file.

I've seen this trotted out time and time again, and at least in this simplified form it's a red herring. If you're going to mutate the argument, it doesn't make sense to give it a default value. If you're going to return a modified form of the input you need to make a copy of it. Doing both is simply absurd.

Disagree. Would say it's a decent violation of expectations for the same instance to be passed into every invocation. Of course, the counterargument is 'know your tools,' which I'm partial to, but the fact that this pops up is an indication it is counterintuitive.

I actually agree it's counterintuitive. But this particular example makes no sense, nobody should be writing real-world code that looks like this in the first place. Either modify the original or return a copy, don't try to do both.

Possibly the most interesting anti-pattern I saw was:

a_list_of_words = "my list of words".split(" ")

I never enquired why, since there were bigger issues in the code e.g. "unit testing" by running the code, taking the result and putting it as the check value. By running repr(value), copying out the string then comparing self.assertEqual(repr(value), '[<Object1: unicode_value>, ...]')

I do that all the time in the interpreter, especially when slicing pandas DataFrame objects, e.g.:

    df_subset = df['date buyer nwidgets'.split()]

That is far easier to type than the explicit list, with all its punctuation. Now, it's definitely weird that they did a `split(" ")` rather than just using the default, but the idea is the same.

I do try to strip stuff like that out before I put it into a script, replacing it with the explicit list, but I'm never sure if that actually improves anything. It's not as if the explicit list is any easier to read.

It's not weird, it's wrong:

    In [1]: "a    string     r".split(" ")
    Out[1]: ['a', '', '', '', 'string', '', '', '', '', 'r']

    In [2]: "a    string     r".split()
    Out[2]: ['a', 'string', 'r']

In their case though that wouldn't have been a problem as each word was split on a single space.

Split without argument is equivalent to:

    >>> filter(None, " quick   hack for  split".split(" "))
    ['quick', 'hack', 'for', 'split']

Yeah, or re.split(r'\s+', ...), or something - the issue here is that you need to know about this.

From the comment a few levels up I understood that the code which used the str.split with " " argument didn't signify that someone who written it knew about its semantics. If he did and it was really what was intended then ofc it's completely ok, but if not, it can easily lead to bugs.

For example, if the user is required to input several ints separated with whitespace, this:

    map(int, input_str.split())

will rise only in expected cases, while this:

    map(int, input_str.split(" "))

can lead to rejecting correct input just because someone pressed space twice. It's very frustrating for the user, too, because whitespace are hard to spot visually.

So, I don't know if this qualifies as antipattern, but I think if I saw .split(" ") instead of .split() in the code I'd at the very least expect the comment explaining why it's used.

I don't mean to be pedantic, but a list (I am assuming df is a list) requires an int.

(That sentence I wrote about using hashable types need not apply, sorry!)

If you ran that code, you would get this error:

    TypeError: list indices must be integers, not list

That's a pandas dataframe (idiomatically denoted df), not a list. It has funky slicing properties, and he's selecting columns of the dataframe in a perfectly valid way.

How is this a real "anti-pattern"?

Inefficent, or bizarre way to do it maybe.

Anti-pattern is supposed to mean something more, though.

In this case there are no adverse effects and no ambiguity -- so, I guess the programmer was just lazy to construct the list.

Why even use a list here? Tuples are for immutable/constant data.

a_tuple_of_words = ("my", "tuple", "of", "words")

a_tuple_of_words = "my", "tuple", "of", "words"

...because it's a list? Tuples were supposed to have a structure (at least that's what all the rest of the world thinks of them), so iterating through combination of apples, cars and languages makes no sense whatsoever.

But yes, Python misses entirely the point of tuples, treating them as read-only lists.

http://dozzie.jogger.pl/2014/04/11/python-tuples-the-useless...

I'm not sure what you're getting at here, or what you are expecting tuples to be like. They can have as much "structure" as you need--they're just a collection.

Lighter-weight, immutable collections have a use case. The code in OP appears to be one where it makes sense. I follow the rule where variables are mutable IFF they need to be mutable.

Tuples by pythonists are used as they were mere lists, just immutable. This is clearly displayed by Python's own interface.

For the rest of the world, tuples are not immutable lists. They are tuples, i.e. collections of "objects" that could share nothing about their type. Tuples often are not even iterable! (Erlang, Haskell)

The fact that tuples in Python can have as much structure as one wants is derived from dynamic typing, not from the tuples' nature. The same you could say about Python's lists.

This is a really subtle issue. It takes to know more languages to see it clearly.

If it's so subtle, does it matter? This sounds like you just have a problem with the word "tuple" applied to an object that behaves differently from tuples in a statically-typed language.

Would you feel better if they named it "ImmutableList" instead?

Can't speak for GP, but I would [feel better with that name].

(Although I agree with you that statically-typed-language-tuples don't seem to make sense in Python.)

But hey... Python's weird choice of how to name the ImmutableList could be worse, right?

For example, someone could be malicious enough to call their general-purpose associative array a "hash", just because a hashmap (note: not a hash) is a good implementation for large associative arrays. Wow, that'd be hilariously misleading, wouldn't it? Good times!

Or imagine someone was silly enough to name their auto-resizing arrays "vectors", even though in all previously existing contexts a "vector" is a sort of thing which absolutely cannot be meaningfully resized/extended. Ha. Think of the tiny cognitive burden placed on generations of future programmers-who-study-math, trying to juggle these two very-similar-but-distinct concepts, multiplied by the number of such future programmers. Amazing practical joke, right?

/rant

Not really that important, but I think map is a better name than associative array.

No, it's not a problem with word "tuple" behaving differently from statically-typed language. It's a problem with word "tuple" behaving differently from all the rest of the world.

Yes, I would feel better if it was named "ImmutableList" or any other way that is not misleading about the purpose.

I'm sorry, I don't see clearly what a tuple should be. What would be different about Python tuples if they were true tuples?

In most languages you can't usually:

1. Iterate over a tuple

2. Convert a list to a tuple

3. Construct a tuple of a length not known at compile-time

Python allows these because "why not?" but it does break their "one and only one way to do it" rule and confuses beginners a hell of a lot.

There are definitely borderline cases. For instance, should a Vector be a list or a tuple? A Vec3 type is obviously a tuple, but a large Vector destined for BLAS is obviously a list.

> Python allows these because "why not?"

No, it allows them because the distinction that those restrictions are founded on is only useful in a statically-typed languages, and Python isn't statically typed.

> For instance, should a Vector be a list or a tuple?

A real vector/array should be its own data type (probably implemented in a C, or similar low-level, extension) that happens to implement the interface expected of an indexable, iterable collection, neither a list nor a tuple.

So why does Python distinguish between a list and a tuple at all?

One is mutable, one is not. Performance. Also, look up namedtuple. Useful for returning multiple values.

Because the distinction between a mutable list and an immutable list is still meaningful in a dynamic language like python.

> [this] distinction [...] is only useful in a statically-typed languages

...like Erlang.

Yeah, while snarkily made its a good point that Erlang does make use of it without being statically typed.

There is a deep difference that goes beyond use of tuples in language approach between Python and Erlang here where it comes to types in which Erlang, while dynamically typed, has a deep concern for types in its pattern matching system to make path decisions while Python is very much centered on using dynamic OO techniques -- how objects respond to messages -- to do that.

So I'd still say its the same kind of deep language approach difference at work.

Have you looked at named tuples? They shipped in the python standard library sometime in the last few years (they are at least in 3.3) and are clearly intended for storing structured data.

A typical rule of thumb in Python land is that heterogeneous data probably belongs in a tuple, so practice goes a little further than immutable lists.

I think you could improve your demonstration of the usage in the standard library by examining a random selection of usages to try to find out what is typical. But maybe you already looked at more than you talk about in the article (and I understand that this might not be an interesting use of your time).

No, I haven't looked at them. Python 2 has them since release 2.6, so it's out of my reach for any practical purpose at the moment (I need to preserve compatibility with Python 2.4).

> A typical rule of thumb in Python land is that heterogeneous data probably belongs in a tuple, so practice goes a little further than immutable lists.

The problem with Python tuples is it's two things mixed: immutable lists and a container for heterogenous data. It's the same situation as JavaScript's objects.

Python tuples are used in both ways.

Even in Haskell, though, people often write all kinds of type-class magic to allow "iterating" over a tuple. For example, a Binary instance over a tuple wants to call "put" on each element.

Haskell's (Oleg's) HList is basically a tuple with iteration/list-like operations.

The distinction between "tuple" and "immutable list" doesn't make any sense outside of a staticly-typed language, since the only difference is what other values a particular value is type-compatible with.

Yes, of course it doesn't. You have just vanished whole Erlang. Or is it statically typed?...

> Tuples were supposed to have a structure (at least that's what all the rest of the world thinks of them)

No, a structure is, you know, a structure -- what C calls a struct. Python calls it a namedtuple. If some people call it just a tuple, well, that's a difference in terminology, but it doesn't mean Python is confused about the concepts, it's just using terminology you're not used to.

Also, if we're going to be pedantic about the meaning of data types, your blog post is wrong about lists. You say "position in the list doesn't matter", but that means ordering doesn't matter, and an unordered collection of similar objects is a set, not a list. Python makes this distinction clear: a list is ordered, a set is not.

> [...] that's a difference in terminology, but it doesn't mean Python is confused about the concepts

No, it menas exactly this. The term "tuple" and its use predates Python. Sorry, no banana.

> [...] your blog post is wrong about lists. You say "position in the list doesn't matter", but that means ordering doesn't matter

Oh, so what's the difference in meaning of element True on position 1 and element True on position 20? Position in list doesn't matter if we're talking about meaning of the elements.

> The term "tuple" and its use predates Python.

References, please? And not mathematical references; programming references. C was using the keyword "struct" long before Python to refer to what you are calling a tuple.

> what's the difference in meaning of element True on position 1 and element True on position 20?

The fact that the index is 1 instead of 20. Both elements have the same type, and might well refer to the same property of some sequence of things; but the index being 1 instead of 20 means the element True is describing that property relative to the first item in some sequence, instead of the 20th item. That's why position in the list makes a difference: the ordering of the items, as well as the type of the items, carries information.

(Of course, in Python the list items don't even have to be of the same type; but most uses of Python lists in practice that I've seen do assume that all the elements are "the same kind of thing".)

> References, please? And not mathematical references; programming references.

ML has had tuples several decades before Python existed.

NB a tuple of one item only requires a trailing comma, and a tuple of zero items is represented as ()

perhaps that line was written by someone used to Perl, where they would have had

@a_list_of_words = qw/my list of words/;

there

Or rubyst:

  a_list_of_words = %w{my list of words}

You'd be sure if they wrote:

    >>> qw = str.split
    >>> qw('my list of words')
    ['my', 'list', 'of', 'words']

This is actually useful: you may want to experiment with different list_of_words in the future and typing the words between [" ", " ", " "] is time consuming. It's also less readable.

> Mutables as default function/method arguments

It would really make sense to change the semantics of Python to fix this issue.

Remove a `static` keyword from C (used inside a function) while you're at it...

Change them how, to no longer have functions be first class objects? The behavior of mutable default arguments is clear if you know how Python treats function objects. Any "fix" would handicap the language.

Can you clarify?

    def foo(default_arg = []):

Why can't that just be shorthand for:

    def foo(default_arg = ParamNone):
        if default_arg == ParamNone:
            default_arg = []

How would that break first class functions?

How would the above work for duck-typed lists, for example? How would the language "know" which types are mutable?

As a minor point, use "default_arg is ParamNone", since "==" probably won't do the right thing.

What breaks is something like:

  def foo(default_arg = slow_f()):
    pass

Under the shorthand gets turned into:

  ParamNone = object()

  def foo(default_arg = ParamNone):
    if default_arg is ParamNone:
        default_arg = slow_f()
    pass

This is fine, since everyone would know that the shorthand means to not put slow code there. Instead, people will start writing it as:

  _foo_arg = slow_f()
  def foo(default_arg = _foo_arg):
    pass

Of course, then what happens with:

  _foo_arg = slow_f()
  def foo(default_arg = _foo_arg):
    _foo_arg = 5

? Under expansion it becomes:

  _foo_arg = slow_f()
  def foo(default_arg = ParamNone):
    if default_arg is ParamNone:
        default_arg = _foo_arg
    _foo_arg.add(5)

This violates Python's scoping rules, because _foo_arg is now being used in local scope instead of global scope. Eg:

  >>> def f(x=None):
  ...   if x is None:
  ...     x = spam
  ...   spam = 3
  ... 
  >>> spam = 9
  >>> 
  >>> f()
  Traceback (most recent call last):
    File "<stdin>", line 1, in <module>
    File "<stdin>", line 3, in f
  UnboundLocalError: local variable 'spam' referenced before assignment

Which means you now need a new scoping rule, just to handle default parameters without making things more confusing.

It also turns what was a simple O(1) offset into a precomputed list into a globals() lookup for many cases.

In Python 3 there's the nonlocal keyword to deal with the scoping thing.

The default arguments thing is worse than a lot of the stuff Python 3 corrected.

I don't see how nonlocal could fix this. Could you explain?

More specifically, given a better 'default arguments thing', how would you interpret:

    x = [2]
    def f(x=x*5):
        x.append(4)

With the earlier conversion it's:

    x = [2]
    def f(x=DefaultArg):
        if x is DefaultArg:
            x = x*5
        x.append(4)

This isn't going to work because the x inside of f() is different than the outside x, and you'll get the error message I mentioned.

If you add a nonlocal, as in:

    x = [2]
    def f(x=DefaultArg):
        nonlocal x
        if x is DefaultArg:
            x = x*5
        x.append(4)

then you'll get "SyntaxError: name 'x' is parameter and nonlocal".

What other solution are you thinking of?

Ah I see. Yeah nonlocal can't fix that.

Why not just memoize the result of the slow function?

I think by your use of "just memoize" that I didn't explain myself well enough. I don't mean to focus on the slow aspect, nor even the function aspect. It could be

   def foo(default_arg = [0]*(256*256)):
     ...

and still get the same namespace issues.

Memoization is not always going to be an available solution. For example, it may be that slow_f() returns a stateless object, so can be reused, while slow_f(x) returns something stateful. You can think of my examples as either using default arguments as a single element memo, or using a module variable for the same. Both premised on the idea that the developer knows enough to make the right decision.

> to no longer have functions be first class objects?

There are other dynamic languages with functions as first class objects which don't share the "mutable default arguments" gotcha.

But having said that, any change regarding this would break backward compatibility.

No, you could just evaluate the expression every time the function is called (like Ruby does) instead of evaluating it once at the time the function is defined (like Python does).

Wildcard imports are OK, if and only if the module is specifically designed with this usage in mind.

Example: nose.tools

Agreed on all counts. However I do find myself using mutables as default arguments sometimes because the generated documentation is clearer.

For example, this is a real method in one of my projects:

  def listen(self, address, ssl=False, ssl_args={}):
      pass

I like the way this turns up in the docs because it's immediately clear that ssl_args needs to be a dict. Otherwise I have to describe it in words.

Well, that's throwing people implementing subclasses under the bus, IMO.

Why not just add @param annotations in your docstrings instead?

> Well, that's throwing people implementing subclasses under the bus, IMO.

If they need to touch this argument in an overridden method and they don't know what they are doing, then yes.

> Why not just add @param annotations in your docstrings instead?

I'm using Sphinx and it renders them separately. I want the empty dict to show up in the function signature.

A single cryptic bug due to this practice will more than negate the minor doc readability benefits you get from that. And there will likely be many more than one cryptic bug.

There are other ways to emphasize it ought to be a dict/mappable. Change its name to be suffixed as "_dict", for example?

You should not ever use mutable default args. The documentation benefit is drastically outweighed by the potential for nasty bugs. http://pythonconquerstheuniverse.wordpress.com/2012/02/15/mu...

In case you're using Python 3, why not write ssl_args: dict, ssl: bool=False?

Also, not knowing what scope a variable has when creating closures.

I agree that bare excepts are bad, however they do not catch Ctrl-C. If you _do_ want to catch Ctrl-C you have to except a KeyboardInterrupt explicitly.

This is untrue (just tested) for at least Python 2.7.x. under Linux, try ... except catches Ctrl-C.

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