Why you should almost never use “is” in Python

It’s really tempting, when you first start to use Python, to use “is” rather than “==”.  It’s a bit more readable, and it feels like it should just work, especially when you’re dealing with integers. In a language that uses “or” and “and” instead of “||” and “&&”, it seems logical to use “is” instead of “==”. And if you try “is” with small integers, or even with short strings, you might be lulled into thinking that you should use “is” in lots of places.

But you shouldn’t.  Really, in almost no case, should you use “is”; rather, you should almost certainly use “==”.  In fact, there’s only one case in which most Python programmers should be using “is”, and that’s to check to see if something is None.

In this blog post, which is the result of many questions and discussions I’ve had with students in my Python classes, I’m going to try to describe the reasons for this — and along the way, describe some parts of how Python’s objects are allocated, and what we mean when we say that two objects are “the same.”

Let’s start with the basics: Everything in Python is an object. Every object in Python has a unique ID number, which we can retrieve from an object by using the built-in “id” function:

>>> id(5)
140236457829784

>>> id('abc')
4503718088

>>> id([1,2,3])
4504494160

Now, if two variables are pointing to the same object, they will (not surprisingly) return the same ID:

>>> x = [1,2,3]
>>> y = x
>>> id(x)
4504494160
>>> id(y)
4504494160

Given that x and y point to the same list, changes to the list will be reflected in both variables:

>>> x[0] = '!'
>>> y[1] = '?'
>>> x
['!', '?', 3]
>>> y
['!', '?', 3]

In such a case, it’s pretty clear that x and y are both pointing to precisely the same object. They aren’t just equal in value; they are one and the same — aliases for one another.

We can ask Python if this is true by using the “is” operator, also known as the “identity operator.” “is” doesn’t compare the values of x and y. Rather, it checks to see if x and y have the same ID. If so, then they are the same object. If not, then they aren’t. It’s as simple as that. Perhaps it goes without saying, but two objects that “is” each other are also “==” to each other, since an object’s value should be equal to itself:

>>> x == y
True

>>> x is y
True

>>> id(x) == id(y)
True

The above code shows that x and y have the same ID. This means that they “is” each other; we’re dealing with two names for the same object. Their values are thus equal, which is what “==” checks.

Again: The “is” operator returns “True” if two names are referring to the same object. And the “==” operator returns “True” if two names point to objects that contain the same value.

The most common usage, by far, is when we want to know if something is None. True, we would use “==”. But in both readability and speed, “is None” trumps “== None”. So your code should generally say:

if x is None:
    print("x is None!")

It shouldn’t surprise us to find out that “is” is faster than “==”. After all, “is” is implemented in C, and is a simple comparison of the IDs of the two objects. No function call is needed, and we certainly don’t need to compare the values of the two objects, which can also take some time.

The use of “is None” works because the None object is a singleton in Python. No matter what you do, id(None) will always return the same value. (Note that this value won’t stay constant across different invocations of Python.)  In other words:

>>> id(None)
4315260920

>>> id(None)
4315260920

>>> x = None
>>> id(x)
4315260920

What happens if you try to create a new instance of None? Well, we would first have to find out None’s type:

>>> type(None)
<type 'NoneType'>

Unfortunately, NoneType isn’t a defined identifier in Python:

>>> NoneType
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'NoneType' is not defined

So if we want to create a new instance of None, we’ll need to do it ourselves:

>>> type(None)()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: cannot create 'NoneType' instances

Aha.  Well, that’s a shame. But I was using Python 2.7 in the above example. What if I try Python 3?

>>> type(None)
<class 'NoneType'>

>>> NoneType()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'NoneType' is not defined

>>> x = type(None)()
>>> type(x)
<class 'NoneType'>
>>> x is None
True

So no matter how you slice it, None is a singleton. Which is why you can (and should) use “is None”, rather than “== None”, in your code.

But what happens if you decide that you want to use “is” in other places? The problem is that it will sometimes work. That “sometimes” is because “is” exposes some of Python’s internal optimizations in ways that can be a bit surprising.

Strings are how I was initially introduced to the difference between “==” and “is”, and the danger of using “is” over-zealously. Two equal strings should be “==”, but are they “is”?

>>> x = 'a' * 5
>>> y = 'a' * 5
>>> x == y
True
>>> x is y
True

Well, that’s interesting — and I got the same result in Python 2.7, 3.4, and also in PyPy. But why should this be the case? One possibility is that strings are immutable, and that having Python use a single object for each string that we create, would be efficient. And indeed, this is true — so long as the string is short:

>>>> x = 'a' * 5000
>>>> y = 'a' * 5000
>>>> x == y
True
>>>> x is y
False

The above, which works the same in Python 2.7, 3.4, and in PyPy, demonstrates that Python won’t reuse just any string that we have created. There is a limit.  I experimented with things a bit, and I found that 21 is the magic length at which strings are no longer “is” to one another. That is:

>>> x = 'a' * 20
>>> y = 'a' * 20
>>> x is y
True

>>> x = 'a' * 21
>>> y = 'a' * 21
>>> x is y
False

The above was true in Python 2.7 and 3.4, and also in PyPy. However, I also found some seemingly weird behavior, which is undoubtedly because of the way in which Python byte-compiles and then executes for loops:

>>> for i in range(15,25):
        x = 'a' * i
        y = 'a' * i
        print("[{0}] x is y: {1}".format(i, x is y))

[15] x is y: False
[16] x is y: False
[17] x is y: False
[18] x is y: False
[19] x is y: False
[20] x is y: False
[21] x is y: False
[22] x is y: False
[23] x is y: False
[24] x is y: False

Wow, that’s kind of strange, no? Indeed, in a for loop, I found that the only number for which the two strings were “is” to one another was 1:

>>> for i in range(0,10):
...     x = 'a' * i
...     y = 'a' * i
...     print("[{0}] x is y: {1}".format(i, x is y))
...
[0] x is y: False
[1] x is y: True
[2] x is y: False
[3] x is y: False
[4] x is y: False
[5] x is y: False
[6] x is y: False
[7] x is y: False
[8] x is y: False
[9] x is y: False

At the same time, if you create a long literal string and assign it to a variable, you’ll likely find that the strings are “is” to one another:

>>> x = 'aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa'

>>> y = 'aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa'

>>> x is y
True

(Forgive the re-formatting that WordPress did to the above assignments; in Python, they were both on one line.)

I’m not sure what is going on here, but it just goes to show that you really shouldn’t use “is” unless you know what you’re doing.  And even if you think that you know what you’re doing, you might still be surprised!  Bottom line: Using “is” on strings is almost always a bad idea.

Now, this is generally something that we don’t need to think or care about very much. But let’s say that you’re working with large strings, and that these strings might repeat themselves on occasion. In such a case, you will end up with many copies of the same string. Python helps us to solve this problem by “interning” strings. Interning is a technique that has been around for many years in the programming world, which allows us to store only one copy of any given string. In Python 2, we use the built-in “intern” function. In Python 3, we must use sys.intern; intern is no longer a builtin.

“intern” takes a string (and only a string) as a parameter. It returns a reference — either to a new string that was created, or to a string that was already allocated. Thus, the length of the string doesn’t matter; even in the case of a long string, it will only be allocated a single time:

>>> from sys import intern     # Python 3 only
>>> x = intern('a' * 5000)
>>> y = intern('a' * 5000)
>>> x is y
True

As you can see, using “intern” guarantees that every unique string is allocated only once. If you use “intern” on the same string a second time, Python returns a reference to the first string.

Python uses “intern” internally for a variety of purposes.  If you’re working with long strings that repeat themselves, then it might be worth using intern. But for the most part, Python creates and allocates so many objects that a few strings here and there are probably not going to make a difference.    Certainly, you should only use “intern” once you have identified bottlenecks.

You might think that even if strings are allocated multiple times, and are thus not “is” to one another, at least integers are going to be identical. After all, Python wouldn’t allocate new objects for numbers, would it?

We can test this pretty easily, of course:

>>> x = 200
>>> y = 200
>>> x is y
True

Well, that’s encouraging, right?  Let’s try something bigger:

>>> x = 2000
>>> y = 2000
>>> x is y
False

So yes, it turns out that even integers that are equal aren’t necessarily pointing to the same object.   As Amy Hanlon pointed out in her fantastic talk about Python “wats”, this is because Python pre-allocates a number of integers. If your integer is within that range, then they will use the same object, and be “is” to one another. But if you’re outside of that range, then you’ll have two separate objects. Unless, of course, you allocate them in the same line of code:

>>> x = 2000; y = 2000
>>> x is y
True

Have I mentioned that you really shouldn’t use “is” to compare objects except for None? I hope that you’re increasingly convinced.

I’ll close this post with a bit of mischief: In theory, if two objects are “is”, then they’re pointing to the same object — which means that they should be identical to one another, and thus also give us a True response to “==”.  While Python doesn’t allow us to redefine “is”, we can redefine what an object says when we try to compare with using “==”:

>>> class Foo(object):
...     def __eq__(self, other):
...         return False
...
>>> f1 = Foo()
>>> f2 = f1
>>> f1 is f2
True
>>> f1 == f2
False

I cannot think of a situation in which this would be a desirable thing to do. But it’s fun, and allows us to sharpen our understanding of the difference between “==” and “is”.

Free Webinar on June 23rd: Introduction to Regular Expressions

If you’re a programmer, then you have likely heard about regular expressions (“regexps”) before. However, it’s also likely that you have tried to learn them, and have found them to be completely confusing. That’s not unusual; while regular expressions provide us with a powerful tool for analyzing text, their terse, dense, and cryptic syntax can make the effort not seem worthwhile.

On June 23rd, I’m going to be offering a one-hour free Webinar introducing regular expressions, showing how they can make your code more powerful and expressive.

While I’ll mostly be using Python, I’ll also show some other languages and platforms (e.g., Ruby, JavaScript, and the Unix “grep” command).

My demo and discussion will be about an hour long, and will be followed by ample time for Q&A.  My previous Webinars have been lots of fun; I hope that you’ll join in!  You can get (free) tickets at EventBrite.

And hey, if you’re an independent consultant, you can get a double dose of me on that same day; we Freelancers Show panelists will be doing our monthly Q&A just beforehand.  Come and get your questions about consulting answered by our panel of experts!

I look forward to seeing you at one or both of these events!  If you have any questions, you can e-mail me or contact me on Twitter as @reuvenmlerner.

New ebook: Jewish guide to visiting China

Jewish Guide to Visiting ChinaAs many people know, I’ve visited China seven times over the last three years, traveling there to give courses in Python and Ruby. I just got back from my most recent trip, and found it to be as fun and exciting as ever. You could say that I’ve gotten a bit obsessed with the country; I read books about China, have been taking daily Chinese lessons since August, and publish a free weekly newsletter (Mandarin Weekly) with links to useful resources for people learning Chinese.

Given that I keep kosher and Shabbat, other religious Jews are increasingly asking me for advice on what, where, and how they can be Jewishly observant when visiting China on business or pleasure. No one in China is likely to know or care about such subjects, let alone know anything about Judaism, so it can be a bit daunting to visit there for the first time.

I’ve collected my advice into a 40-page ebook, the “Jewish guide to visiting China.” If you’re a religiously observant Jew who will be visiting China for short periods of time, then I believe this guide can significantly reduce the time (and stress) you’ll need to invest before your trip.

I’m just launching it now — and for the first week it’s online, I’m offering a discount coupon (“YouTaiRen” — aka 犹太人 — the word for “Jew” in Chinese) giving 20% off of the normal $6 price. This price includes PDF, Mobi, and ePub formats, which should suit any computer or ebook reader.

Again: The Jewish guide to visiting China, now available for 20% off with the “YouTaiRen” offer code.

I expect that the book will expand significantly over time; if you purchase this book, you’re automatically entitled to updates and upgrades.

I welcome comments, suggestions, and additions!

Free one-hour Webinar about Python’s magic methods on May 6th

I’ll be giving another free one-hour Webinar about Python — this time, about the magic methods that Python offers developers who want to change the ways in which their objects work.

We’ll start off with some of the simplest magic methods, but will quickly move onto some of the more interesting and advanced ones — affecting the way that our objects are compared, formatted, hashed, pickled, and sauteed.  (OK, maybe not sauteed.)  Some familiarity with Python objects is expected, but not too much advanced knowledge is necessary.

Register now at EventBrite; if you have any questions, please contact me at reuven@lerner.co.il, or as @reuvenmlerner on Twitter.  It should be a lot of fun; I hope to see you there!

Is it hashable? Fun and games with hashing in Python

One of the basic data types that Python developers learn to use, and to appreciate, is the dictionary, or “dict.” This is the Python term for what other languages call hashes, associative arrays, hashmaps, or hash tables. Dictionaries are pervasive in Python, both in the programs that we write, and in the implementation of the language; behind every namespace or object, at least one dictionary is behind the scenes.

Dictionaries are fairly easy to use, once you get used to the rules of the road:

  1. A dictionary contains pairs, not individual elements. Each pair has two elements, a “key” and a “value.” So given a dictionary d, len(d) will return the number of pairs, not the number of individual elements.
  2. You can think of the key as a sort of index. Just as we use numeric indexes to retrieve elements of a string, list, or tuple, we use a dict’s keys to retrieve its values.
  3. The retrieval is one-way. You can get a value via its key, but you cannot get a key via its value.
  4. The retrieval takes constant time, aka O(1). You can use the “in” operator to find out if a key exists in a dictionary. If you retrieve a key that doesn’t exist, you’ll get a KeyError exception.
  5. The key must be hashable, and (if a container, such as a tuple) may only contain other hashable objects.
  6. The values may be any Python types or sizes. You can have a dict of strings, but also a dict of lists, tuples, dicts, modules, or any other objects.
  7. The keys of a dictionary are unique. If you assign d[‘a’]=1 to the dict “d”, the key “a” now exists, with a value of 1. If the key “a” already existed, then its previous value is lost.
  8. The key-value pairs in a dictionary are not ordered in any meaningful way. Do not depend on the order of the pairs in a dictionary.

To anyone familiar with dicts, or with hash tables in other languages, most of the above rules make a great deal of sense. Indeed, most of them follow naturally from the implementation of dicts: When you store d[‘a’] = 1, the dict “d” takes the key “a” and invokes the hash function on it. The result of the hash function is a number, which indicates where in the hash table the key-value pair should be stored. This is the key (no pun intended) advantage of a dictionary, and the secret of its lookup speed: The result of applying the “hash” function on our key determines where the key-value pair will be stored. Python can then jump to that location in memory, and retrieve the value associated with the key.

This also explains why you can use keys to retrieve values, but not the reverse: The location of a value in memory depends completely on its key. Moreover, while keys must be unique, values don’t have to be.

Furthermore, this explains why pairs in a dict don’t seem to be ordered in any predictable way; their ordered is determined by the hash function, which is deliberately designed to provide hard-to-predict results.

For example, I can create a simple dictionary:

>>> d = {'a':1, 'b':2, 'c':3}
 >>> d
 {'a': 1, 'c': 3, 'b': 2}

As you can see, the printed representation of our dictionary shows the keys in the order ‘a’, ‘c’, and ‘b’, rather than order or the order in which they were inserted. Assigning to the dictionary either replaces an existing pair (if I reuse a key) or adds a new pair:

>>> d['a'] = 100
 >>> d
 {'a': 100, 'c': 3, 'b': 2}
 >>> d['z'] = [1,2,3]
 >>> d
 {'a': 100, 'c': 3, 'b': 2, 'z': [1, 2, 3]}

Almost all of this matches the rules for dict-like structures in other languages — except for rule #5, the requirement that the keys be hashable. (Or if we’re dealing with container objects, that the contained elements be hashable.) It’s reasonable to ask why this is forbidden. There aren’t a lot of times when I would like to use a list, set, or dict as a dictionary key, but it does happen. Why does Python prevent me from doing so?

The answer has to do with predictability: If I could use a list as my dictionary key, then there would be the chance of the list changing after storing it. In such a case, the list’s current hash value will be different than its previous hash value — meaning that the list will be located somewhere other than where it should be. In such a case, the key-value pair would be “lost” inside of the dict.

This can actually happen in Ruby, which doesn’t restrict the data types which can be used as keys. For example:

myarray = [1,2,3]    # create a Ruby Array 
h = {myarray => 1}   # use the array as a key

h[myarray]           # What value is associated with myarray?
   => 1              # We get 1 back, as expected! Yay!

Ruby stores our name-value pair inside of the hash, its equivalent of a dict. However, I can modify the array that is being used as a hash key:

myarray << 4        # append 4 to myarray

    => [ 1, 2, 3, 4 ]

When we stored the name-value pair in myarray, the Array had three elements. Now it has four, thus giving it a new hash value. After modifying myarray, we can ask Ruby to retrieve the value associated with it in h:

h[myarray]         # Get the value for key "myarray"
     => nil        # nil means non-existent key

In other words, the hash still has the key “myarray”, but the key-value pair is stored in the location determined by myarray.hash when we first stored it, not in its current incarnation.

Ruby’s solution to this problem is to provide a “rehash” method, which tells a hash to go through its contents, and recalculate the keys’ hash values and locations. Once you do this, the data returns:

h.rehash  # recalculate positions
    =>  { [ 1, 2, 3, 4 ] => 1 }

h[myarray]
    => 1

We thus see that in Ruby, we’re allowed to use mutable data structures as keys. The advantage is that we’re not limited, but the disadvantage is that keys might get changed, and thus provide incorrect search results.

Python, many years ago, solved this problem a different way: Instead of allowing us complete flexibility in our hash keys, Python restricted us, to (largely) immutable ones. Thus, we can use None, True, False, integers, floats, strings, and tuples — although ints and strings are the most common, in my experience. If we try to store a key-value pair in a dictionary, Python checks to make sure that the key is a hashable type:

>>> mylist = [1,2,3]

>>> d = {mylist:1}
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'list'

The “unhashable” error message that we get isn’t from the assignment to d, but rather from the call to hash() that Python makes on “mylist”. We can see this if we try to invoke the hash function directly:

>>> hash(mylist)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'list'

It’s not really true, as we’ve seen, that lists are inherently unhashable. Rather, Python decided long ago that it would refuse to hash anything whose value might be subject to change, to avoid elements getting lost.

The hash function in Python has been described before, in more detail (and with greater knowledge) than I could provide. There are two aspects to Python’s hash function, which I’m not in a position to criticize, but which do seem strange to me:

  1. Hash functions, by their very nature, are supposed to be one-way, deterministic, but fairly unpredictable. That is, if I know the output of hash(‘a’), I shouldn’t be able to easily know what hash(‘b’) or hash(‘c’) is. But hash(‘a’) should always return the same value. In Python, this is the case for strings and tuples. But hash(), when handed an int, returns that int. Thus, hash(1) is 1, hash(100) is 100, and hash(255) is 255. This strikes me as a bit strange, and seems to violate one of the basic rules of hashing. I can only conclude that either I don’t know much about hash functions (which is quite possible), that Python doesn’t expect us to use many integers as dictionary keys, or that it just doesn’t matter that much.
  2. The hash function apparently returns -1 when it encounters an error. Thus, the hash values of both -1 and -2 are -2.

The result of a hash function doesn’t need to be unique, but it does need to evenly distribute the results, such that we’ll minimize collisions. That is, it’s possible that hash(‘a’) and hash(‘b’) will return the same value — but it should be hard to figure out which values will give us the same results. If, by some chance, all of your keys have the same hash value, then you end up with a “collision.” This is invisible to the user of the dict, except that the lookups suddenly become much slower. Imagine a dict in which 100 keys all have the same hash value; our lookup speed suddenly becomes O(n), like a list, rather than O(1), which is theoretically possible in a dict.

This apparently became an issue several years ago, when there were some attacks against Web sites running Python. Web applications often use dicts to pass incoming parameters, which means that if you choose your keys cleverly enough, you can cause a massive slowdown on a site, in a denial-of-service attack.

The solution is to add a random seed to the hashing algorithm. This isn’t implemented in Python by default, but can easily be added by invoking Python 2.7 with the -R command-line parameter, or Python 3.x with the PYTHONHASHSEED environment variable set.

Thus, in Python 2.7:

$ python -R
>>> hash('a')
-5027793331667802690
>>> hash('b')
-5027793332354350531
>>>

$ python -R
>>> hash('a')
-4154372447873558006
>>> hash('b')
-4154372448337085303
>>>

Notice how, thanks to the -R parameter, we force Python to re-seed its hash function, thus reducing the chance of a successful attack.

Python 3 took this a step further, by using an environment variable. If you set PYTHONHASHSEED  to “random”, then it behaves like Python 2, above. But if you set PYTHONHASHSEED to a numeric value, then the hash function is seeded with the number you provide. This makes it easier to test your code, but also to enjoy the extra security that the randomized hash keys provide.

Now, you would think that from everything I wrote above, that if I write my own class, it won’t be hashable. But it turns out that this is not the case:

>>> class Foo(object):
        pass

>>> f = Foo()
>>> hash(f)
273483861

According to the Python documentation, user-defined classes are hashable by default; the hash value of such an object depends on the object’s unique ID number, which we can get via the built-in “id” function. This means that the hash value of a user-defined object won’t change, regardless of any changes you might make to its attributes.

But let’s say that I want to have the hash reflect the attributes. According to the Python documentation, this means that I should define both the __hash__ method (which the built-in “hash” function will call on our object, and which must return an integer) and the __eq__ method, to check if two things are equal (since two equal objects should have equal hashes, too). I’m going to define a simple class, along with a __hash__ method:

>>> class Foo(object):
        def __init__(self, x):
            self.x = x
        def __hash__(self):
            return hash(self.x)

>>> f = Foo('a')
>>> hash(f)
12416037344
>>> hash('a')
12416037344

As the above code demonstrates, our object now returns the hash value of whatever is set on its “x” attribute. There is no difference between invoking hash(‘a’) and hash(f), assuming that f.x is ‘a’.

So now let’s put our object in a dictionary:

>>> d = {f:1}
>>> d[f]
1

So far so good, right? But now let’s be a bit evil, and change the value of f.x:

>>> f.x = 'abc'
>>> d[f]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
KeyError: <__main__.Foo object at 0x109987590>

What happened? Well, we forced Python to be like the Ruby example that I provided earlier: When we created d, and used f as a key, Python stored the pair {f:1} based on the value of hash(f). But then we changed the value of f.x, which means that the value of hash(f) has changed, Which means that now, when we invoke d[f], Python will complain that there is no such key. The only way to get this key back is to use d.keys() or d.items(), which will return the key. But our ability to retrieve our value via the key, or even to check if our key exists, is now gone:

>>> f in d
False

We can have even more fun, by ensuring that the value of __hash__ changes every time we invoke it:

>>> import random
>>> class LoseMe(object):
        def __hash__(self):
            return random.randint(1,1000)

>>> x = LoseMe()
>>> hash(x)
374
>>> hash(x)
50

Now, the odds are pretty good that when I stick this object into a dictionary, I won’t be able to get it back:

>>> x = LoseMe()
>>> d = {x: 1}
>>> x in d
False
>>> x in d
False

But of course:

>>> list(d.keys())
[<__main__.LoseMe object at 0x109997a10>]

Should you ever define __hash__ to return a random value? Almost certainly not. And yet, I’d like to think that knowing how to do such a thing is both interesting and provides insights into how Python implements one of its core features.

 

Free Webinar in object-oriented Python on April 1st, 2015

Join me for an hour-long free Webinar about object-oriented Python on April 1st, 2015. I’ll discuss how to use and create classes in Python, how attributes form the core of Python’s object system, and how you should (and shouldn’t) think about Python’s objects. There will be plenty of time for Q&A. Come and join me; it’ll be a lot of fun!

You can register for the Webinar at Eventbrite:

https://eventbrite.com/event/16312775952/

I look forward to seeing you there!

How not to write an error message

Users and programmers see error messages very differently.

When a user sees an error message, they think, “Oh, no.  Something went wrong.”  Rarely, in my experience, does the user think to read the error message, or to use it as a clue toward what might have happened.  Technology is so opaque, so hard to understand, and so seemingly random that people have stopped paying attention to error messages.  The situation hasn’t been helped by the “blue screen of death,” or messages that say, “Error -16,” or the like.  The more people get such messages, the more they’re likely to tune them out.

When clients ask me for help with a problem, I often ask them to tell me what error message they saw. This question often surprises them; they don’t expect that the error message will tell them what their next step could be. But in good software, it will.

Programmers thus see error messages as helpful hints toward how a solution can be solved. (Of course, programmers and engineers are generally interested in solving problems — something that the public at large doesn’t necessarily share.)  When I see an error message, I ask, “Hmm, let’s see if this is telling me what I can do to remove the problem.” I see the error message as a helpful hint that the system’s programmer left for me, rather than an insult or yet more proof that technology is fickle.

But it turns out that when programmers are new or inexperienced with a technology, they often exhibit the same behavior as novices. This week, I taught a two-day course in Git. Now, Git is not exactly known for being an easy-to-understand system — but as I showed my students, there are many times when the error message does tell us something useful. Perhaps it’s using sui generis Git-flavored technobabble, but once you understand how Git works behind the scenes, the message will actually make sense.

My point is that even if error messages are ignored by the majority of users, they can and should be written such that people can benefit from them. Even if the number of people who will read these messages is small, you should at least give them a hint, and indicate why things when wrong.

Indeed, a good error message should say: (1) what inputs were provided that caused this error, (2) what was wrong with those inputs, and (3) what you can do to solve the problem. It’s totally fine to log lots of additional information to a logfile or database for further analysis later on. But to the degree that it’s possible, error messages should try to help people to help themselves.

That’s nice in theory, of course, In reality, there are the completely, maddeningly, stupid error messages that give all programmers a bad name.

Case in point: About two years ago, I bought a 12-trip train ticket (“kartisiya”) from Modi’in (where I live) to Bnai Brak (where I had a client). I figured that I would make enough trips to Bnai Brak that it was worth getting these tickets, and putting them on my electronic ticket card, known in Israel as a “Rav kav.” But it turned out that it was actually faster for me to get off at one of the Tel Aviv train stations, and then walk to this client’s office.

A few days ago, I decided that if I hadn’t used these tickets in two years, I should probably get them refunded. Or changed to another city. Or something.  Unfortunately, because they were on my Rav Kav, no station would refund my money. Nor could they change the tickets to be between Modi’in and another city. I was stuck with 12 tickets to a train station that I wasn’t likely to need or use in the near future.

Fortunately, Israel Railways has a fancy Web site. I went there, and found that I could submit questions to their help line via a Web form. Note that Israel Railways has removed phone numbers and e-mail addresses from their “contact us” page; if you want to contact them, you’re encouraged to (1) use a Web form, (2) send a fax, or (3) send postal mail.

So I went to the Web form, filled in my story about my 12-trip ticket to Bnai Brak, provided lots of personal details, and was greeted with the following message:

train-fail

If you don’t read Hebrew, then here’s my quick transaction of the central text:

Form for public queries

Dear customer,

The system wasn’t able to handle your query.

Thanks for contacting us!

So, which is it? Are they thanking me?  Or telling me that something went wrong?  (Or both?) And did something go wrong because of a failure on my part, or on theirs?  Should I re-submit my query?  (I did; I got the same failure a second time.)

In the end, I discovered that there is a live-chat option on the Israel Railways site. Surprisingly enough, it was staffed by a real human, who asked me what was wrong.  I told her what I wrote above — and she said that complaint has been registered, and someone will get back to me in the near future.

I believe that they will get back to me. And I believe (hope) that I’ll be able to get my money back on those tickets.

But I keep thinking about the programmer who implemented this error message, and how he or she has, without knowing it, contributed to the general public’s sense that technology is random, unfriendly, and destined to drive us mad.

Are you a programmer? If so, do you consider the content of your error messages when you write them, and what people will do with them if and when they appear? Remember that error messages are meant to be read by people, not machines — and try to write them accordingly.

A quick introduction to implementing Python iterators

When you put a piece of Python data into a “for” loop, the loop doesn’t execute on the data itself.  Rather, it executes on the data’s “iterator.”  An iterator is an object that knows how to behave inside a loop.

Let’s take that apart.  First, let’s assume that I say:

for letter in 'abc':
    print(letter)

I’m not really iterating over ‘abc’.  Rather, I’m iterating over the iterator object that I got from ‘abc’.  That is invisible and behind the scenes, but it happens all the same.  We can get the iterator of any object with the iter() function:

>>> s = 'abc'

>>> iter(s)
<iterator at 0x10a47f150>

>>> iter(s)
<iterator at 0x10a47f190>

>>> iter(s)
<iterator at 0x10a47f050>

Notice that each time we invoke iter(s), we get back a new and different object.  (We can tell, because there is a different address in memory for each one.)  That’s because each iterator is used only once.  Once you get to the end of an iterator object, the object is thrown out, and you need to get a new one.

OK, so what can we do with this iterator object?  Why do we care about it so much?  Because we can invoke the next() function on it.  Each time we do so, we’re basically telling the object that we want to get the next piece of data that it’s providing:

>>> i = iter(s)

>>> next(i)
'a'

>>> next(i)
'b'

>>> next(i)
'c'

So far, so good: Each time we invoke next(i), we ask our iterator object (i) to give us the next element.  But there are only three elements in s, which raises the question of what we’ll get when we invoke next() another time:

>>> next(i)
StopIteration

In other words, Python raises an exception (StopIteration) when we get to the end.  We can now invoke next(i) as many times as we want; we’ll always get StopIteration, which indicates that there is nothing more to get.

You can thus think of a “for” loop as a “while” loop that catches the StopIteration exception, and then leaves the loop when it happens. Consider this function:

def myfor(data):
    i = iter(data)
    while True:
        try:
            print next(i)
        except StopIteration:
            break

Now, this “myfor” function only prints the elements of the sequence it was given, so it’s not really a replacement for loop.  But it’s not a bad way to begin to understand how these things work. Our function starts off by getting an iterator for our data.  It then assumes that we are going to iterate forever on the object, using the “while True” infinite loop. However, we know that when next(i) is done providing elements of data, it will raise StopIteration.  At that point, we’ll catch the exception and return from the function.

Let’s assume that you want to make instances of your class iterable. This means that when we invoke iter() on an instance of your class, we’ll want to get back an iterator.  Which means that we’ll want to get back an object on which we can invoke next(), and either get the next object or the StopIteration exception.

The easiest way to do this is to define both __iter__ (which is invoked when you run iter() on an object) and __next__ (which is invoked when you run next() on an iterator) within your class object. That is, you’ll define __iter__ to return self, because the object is its own iterator.  And you’ll define __next__ to return the next piece of data in turn, or to raise StopIteration if there is no more data.

Remember that in an iterator, there is no “previous” or “reset” or anything of the sort.  All you can do is move forward, one item at a time, until you get to the end.

So let’s say that I want to define a simple iterator, one that returns the elements of a piece of data.  (Yes, basically what you already get built in by Python.)  We can say:

class MyIter(object):
    def __init__(self, data):
        self.data = data
        self.index = 0
    def __iter__(self):
        return self
    def __next__(self):   # In Python 2, this is just "next")
        if self.index >= len(self.data):
            raise StopIteration
        value = self.data[self.index]
        self.index += 1
        return value

Now I can say

>>> m = MyIter('abc')
>>> for letter in m:
        print(letter)

and it will work!

You can take any class you want, and make it into an iterator by adding the  __iter__ method (which returns self) and the __next__ (or in Python 2, “next”)  method.  Once you have done that, instances of MyIter can now be put inside of “for” loops, list comprehensions, or anything else that expects an “iterable” type of data.

If you don’t use “with”, when does Python close files? The answer is: It depends.

One of the first things that Python programmers learn is that you can easily read through the contents of an open file by iterating over it:

f = open('/etc/passwd')
for line in f:
    print(line)

Note that the above code is possible because our file object “f” is an iterator. In other words, f knows how to behave inside of a loop — or any other iteration context, such as a list comprehension.

Most of the students in my Python courses come from other programming languages, in which they are expected to close a file when they’re done using it. It thus doesn’t surprise me when, soon after I introduce them to files in Python, they ask how we’re expected to close them.

The simplest answer is that we can explicitly close our file by invoking f.close(). Once we have done that, the object continues to exist — but we can no longer read from it, and the object’s printed representation will also indicate that the file has been closed:

>>> f = open('/etc/passwd')
>>> f
<open file '/etc/passwd', mode 'r' at 0x10f023270>
>>> f.read(5)
'##\n# '

f.close()
>>> f
<closed file '/etc/passwd', mode 'r' at 0x10f023270>

f.read(5)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-ef8add6ff846> in <module>()
----> 1 f.read(5)
ValueError: I/O operation on closed file

But here’s the thing: When I’m programming in Python, it’s pretty rare for me to explicitly invoke the “close” method on a file. Moreover, the odds are good that you probably don’t want or need to do so, either.

The preferred, best-practice way of opening files is with the “with” statement, as in the following:

with open('/etc/passwd') as f:
    for line in f:
        print(line)

The “with” statement invokes what Python calls a “context manager” on f. That is, it assigns f to be the new file instance, pointing to the contents of /etc/passwd. Within the block of code opened by “with”, our file is open, and can be read from freely.

However, once Python exits from the “with” block, the file is automatically closed. Trying to read from f after we have exited from the “with” block will result in the same ValueError exception that we saw above. Thus, by using “with”, you avoid the need to explicitly close files. Python does it for you, in a somewhat un-Pythonic way, magically, silently, and behind the scenes.

But what if you don’t explicitly close the file? What if you’re a bit lazy, and neither use a “with” block nor invoke f.close()?  When is the file closed?  When should the file be closed?

I ask this, because I have taught Python to many people over the years, and am convinced that trying to teach “with” and/or context managers, while also trying to teach many other topics, is more than students can absorb. While I touch on “with” in my introductory classes, I normally tell them that at this point in their careers, it’s fine to let Python close files, either when the reference count to the file object drops to zero, or when Python exits.

In my free e-mail course about working with Python files, I took a similarly with-less view of things, and didn’t use it in all of my proposed solutions. Several people challenged me, saying that not using “with” is showing people a bad practice, and runs the risk of having data not saved to disk.

I got enough e-mail on the subject to ask myself: When does Python close files, if we don’t explicitly do so ourselves or use a “with” block? That is, if I let the file close automatically, then what can I expect?

My assumption was always that Python closes files when the object’s reference count drops to zero, and thus is garbage collected. This is hard to prove or check when we have opened a file for reading, but it’s trivially easy to check when we open a file for writing. That’s because when you write to a file, the contents aren’t immediately flushed to disk (unless you pass “False” as the third, optional argument to “open”), but are only flushed when the file is closed.

I thus decided to conduct some experiments, to better understand what I can (and cannot) expect Python to do for me automatically. My experiment consisted of opening a file, writing some data to it, deleting the reference, and then exiting from Python. I was curious to know when the data would be written, if ever.

My experiment looked like this:

f = open('/tmp/output', 'w')
f.write('abc\n')
f.write('def\n')
# check contents of /tmp/output (1)
del(f)
# check contents of /tmp/output (2)
# exit from Python
# check contents of /tmp/output (3)

In my first experiment, conducted with Python 2.7.9 on my Mac, I can report that at stage (1) the file existed but was empty, and at stages (2) and (3), the file contained all of its contents. Thus, it would seem that in CPython 2.7, my original intuition was correct: When a file object is garbage collected, its __del__ (or the equivalent thereof) flushes and closes the file. And indeed, invoking “lsof” on my IPython process showed that the file was closed after the reference was removed.

What about Python 3?  I ran the above experiment under Python 3.4.2 on my Mac, and got identical results. Removing the final (well, only) reference to the file object resulted in the file being flushed and closed.

This is good for 2.7 and 3.4.  But what about alternative implementations, such as PyPy and Jython?  Perhaps they do things differently.

I thus tried the same experiment under PyPy 2.7.8. And this time, I got different results!  Deleting the reference to our file object — that is, stage (2), did not result in the file’s contents being flushed to disk. I have to assume that this has to do with differences in the garbage collector, or something else that works differently in PyPy than in CPython. But if you’re running programs in PyPy, then you should definitely not expect files to be flushed and closed, just because the final reference pointing to them has gone out of scope. lsof showed that the file stuck around until the Python process exited.

For fun, I decided to try Jython 2.7b3. And Jython exhibited the same behavior as PyPy.  That is, exiting from Python did always ensure that the data was flushed from the buffers, and stored to disk.

I repeated these experiments, but instead of writing “abc\n” and “def\n”, I wrote “abc\n” * 1000 and “def\n” * 1000.

In the case of Python 2.7, nothing was written after the “abc\n” * 1000. But when I wrote “def\n” * 1000, the file contained 4096 bytes — which probably indicates the buffer size. Invoking del(f) to remove the reference to the file object resulted in its being flushed and closed, with a total of 8,000 bytes. So in the case of Python 2.7, the behavior is basically the same regardless of string size; the only difference is that if you exceed the size of the buffer, then some data will be written to disk before the final flush + close.

In the case of Python 3, the behavior was different: No data was written after either of the 4,000 byte outputs written with f.write. But as soon as the reference was removed, the file was flushed and closed. This might point to a larger buffer size. But still, it means that removing the final reference to a file causes the file to be flushed and closed.

In the case of PyPy and Jython, the behavior with a large file was the same as with a small one: The file was flushed and closed when the PyPy or Jython process exited, not when the last reference to the file object was removed.

Just to double check, I also tried these using “with”. In all of these cases, it was easy to predict when the file would be flushed and closed: When the block exited, and the context manager fired the appropriate method behind the scenes.

In other words: If you don’t use “with”, then your data isn’t necessarily in danger of disappearing — at least, not in simple simple situations. However, you cannot know for sure when the data will be saved — whether it’s when the final reference is removed, or when the program exits. If you’re assuming that files will be closed when functions return, because the only reference to the file is in a local variable, then you might be in for a surprise. And if you have multiple processes or threads writing to the same file, then you’re really going to want to be careful here.

Perhaps this behavior could be specified better, and thus work similarly or identically on different platforms? Perhaps we could even see the start of a Python specification, rather than pointing to CPython and saying, “Yeah, whatever that version does is the right thing.”

I still think that “with” and context managers are great. And I still think that it’s hard for newcomers to Python to understand what “with” does. But I also think that I’ll have to start warning new developers that if they decide to use alternative versions of Python, there are all sorts of weird edge cases that might not work identically to CPython, and that might bite them hard if they’re not careful.

If you enjoyed this explanation, check out my free e-mail course on working with files in Python, or my e-book, “Practice Makes Python,” with 50 battle-tested exercises in Python programming!

My latest side project: DailyTechVideo.com, posting new conference videos every day

If you’re like me, you love to learn. And in our industry, a primary way of learning involves attending conferences.

However, if you’re like me, you never have the time to actually attend them.  (In my case, the fact that I live far away from where many conferences take place is an additional hindrance.)

Fortunately, a very large number of talks at modern conferences are recorded. This means that even if you didn’t attend a conference, you can still enjoy (and learn from) the talks that were there.

However, this leads to a new and different problem: There are too many talks for any one person to watch. How can you find things that are interesting and relevant?

My latest side project aims to solve this problem, at least in part: DailyTechVideo.com offers, as its name implies, a high-quality, thought-provoking talk about technology each day. To date, almost all of the talks reflect the technologies that are of interest to me, which typically means that they are open source programming languages, databases, or Web application frameworks. But I have tried to include conference videos that have provoked and prodded my thinking, and which are likely to be helpful for other professionals in the computer industry. Moreover, I’m hoping to receive suggestions from people who have seen interesting videos in fields with which I’m less familiar (e.g., hardware or robotics), who can help me to improve my own understanding and knowledge.

So if you enjoy learning, I invite you to subscribe to DailyTechVideo.com, and/or to follow its Twitter feed at @DailyTechVideo.

And if you can suggest videos to include, e-mail me at reuven@lerner.co.il, or tweet me at @ReuvenMLerner or @DailyTechVideo. I already have another 4-5 weeks of videos queued up, but I’m always on the lookout for new and interesting ones.