Creating List-Like Objects in Python

In this post, we will be talking about how Python likes to deal with “list-like objects”. We will be diving into some quirks of Python that might seem a bit weird and, in the end, we will hopefully teach you how to build something that could actually be useful while avoiding common mistakes.

Part 1: Fake lists

Let’s start with this snippet.

class FakeList:
    def __getitem__(self, index):
        if index == 0:
            return "zero"
        elif index == 1:
            return "one"
        elif index == 2:
            return "two"
        elif index == 3:
            return "three"
        elif index == 4:
            return "four"
        elif index == 5:
            return "five"
        elif index == 6:
            return "six"
        else:
            raise IndexError(index)

f = FakeList()

A lot of people will be familiar with this:

f[3]
# <<< 'three'
__getitem__

 is the method you override if you want your instances to respond to the square bracket notation. Essentially

f[3]

 is equivalent to

f.__getitem__(3)

What you may not know, is this:

for i, n in enumerate(f):
    print(i, n)
# 0 zero
# 1 one
# 2 two
# 3 three
# 4 four
# 5 five
# 6 six

list(f)
# <<< ['zero', 'one', 'two', 'three', 'four', 'five', 'six']

or this:

'three' in f
# <<< True
'apple' in f
# <<< False

Before I explain what I think is going on, let’s try to tweak the snippet to see how it reacts:

class FakeList:
     def __getitem__(self, index):
         if index == 0:
             return "zero"
         elif index == 1:
             return "one"
         elif index == 2:
             return "two"
         elif index == 3:
             return "three"
-        elif index == 4:
-            return "four"
         elif index == 5:
             return "five"
         elif index == 6:
             return "six"
         else:
             raise IndexError(index)
f = FakeList()
list(f)

Although this would be a reasonable outcome:

list(f)
# <<< ['zero', 'one', 'two', 'three', 'five', 'six']  # wrong

It turns out that the actual result is this:

list(f)
# <<< ['zero', 'one', 'two', 'three']

Let’s try another tweak now:

 class FakeList:
     def __getitem__(self, index):
         if index == 0:
             return "zero"
         elif index == 1:
             return "one"
         elif index == 2:
             return "two"
         elif index == 3:
             return "three"
         elif index == 4:
             return "four"
         elif index == 5:
             return "five"
         elif index == 6:
             return "six"
-        else:
-            raise IndexError(index)
f = FakeList()
list(f)

If you try to run this, it will get stuck and you will have to stop it with ctrl-c. To see why this is the case, let’s tweak some more:

for i, n in enumerate(f):
    print(i, n)
    input("Press Enter to continue")

# 0 zero
# Press Enter to continue
# 1 one
# Press Enter to continue
# 2 two
# Press Enter to continue
# 3 three
# Press Enter to continue
# 4 four
# Press Enter to continue
# 5 five
# Press Enter to continue
# 6 six
# Press Enter to continue
# 7 None
# Press Enter to continue
# 8 None
# Press Enter to continue
# 9 None
# Press Enter to continue
# 10 None
# Press Enter to continue
# 11 None
# Press Enter to continue
# ...

And our final tweak:

 class FakeList:
     def __getitem__(self, index):
         if index == 0:
             return "zero"
         elif index == 1:
             return "one"
         elif index == 2:
             return "two"
         elif index == 3:
+            3 / 0
             return "three"
         elif index == 4:
             return "four"
         elif index == 5:
             return "five"
         elif index == 6:
             return "six"
         else:
             raise IndexError(index)
f = FakeList()
for i, n in enumerate(f):
    print(i, n)

# 0 zero
# 1 one
# 2 two
# ZeroDivisionError: divison by zero

With all of this in mind, let’s try to figure out what Python does when you try to iterate over an object. The steps are, in order:

  1. See if object has an 
    __iter__

    method. If it does, call it and `yield` the results.

  2. See if the object has a 
    __next__

    method. If it does, call it repeatedly,

    yield

    each result until at some point it raises a

    StopIteration

    exception.

    It would be reasonable to assume that Python would give up at this point, but it looks like it has yet another trick up its sleeve:

  3. See if the object has a 
    __getitem__

    method. If it does:
    – Call it with

    0

    ,

    yield

    the result
    – Call it with

    1

    ,

    yield

    the result
    – Call it with

    2

    ,

    yield

    the result
    – And so on…
    – If at some point you get an

    IndexError

    , stop the iteration
    – If at some point you get any other exception, raise it

This explains all our examples:

  • When we removed the 
    elif index == 4

    part, it went straight to the

    IndexError

    and stopped the iteration

  • When we removed the 
    raise IndexError(index)

    part, it went to the end of the body of the method, which in Python means that the method returns

    None

    ;

    None

    is a perfectly acceptable value for

    __getitem__

    to return, so the iteration went on forever

  • When we injected a 
    3 / 0

    somewhere, it raised a

    ZeroDivisionError

    in the middle of the iteration

Lets now revert to our first example, the “correct” one, and try throwing some more curveballs at it:

len(f)
# TypeError: object of type 'FakeList' has no len()

list(reversed(f))
# TypeError: object of type 'FakeList' has no len()

To be honest, the first time I tried these, I expected

len()

 to work. Python would simply have to try an iteration and count how many steps it took to reach an IndexError. But it doesn’t. It probably makes sense since iterable sequences may also be infinite sequences and Python would get stuck. The fact that

reversed()

 doesn’t work wasn’t surprising, especially since

len()

 didn’t work. How would Python know where to start? In fact, when we called reversed(), Python complained about the missing

len()

 of FakeList, not

reversed()

. But it seems that we can fix both problems by adding

len()

 to our FakeList:

 class FakeList:
     def __getitem__(self, index):
         if index == 0:
             return "zero"
         elif index == 1:
             return "one"
         elif index == 2:
             return "two"
         elif index == 3:
             return "three"
         elif index == 4:
             return "four"
         elif index == 5:
             return "five"
         elif index == 6:
             return "six"
         else:
             raise IndexError(index)
 
+    def __len__(self):
+        return 7
f = FakeList()
len(f)
# <<< 7

list(reversed(f))
# <<< ['six', 'five', 'four', 'three', 'two', 'one', 'zero']

So, to sum up. What can we do with our

FakeList

 object?

  1. We can use the square bracket notation (no surprises there): 
    f[3] == "three"
  2. We can call 
    len()

    on it (again, no surprises):

    len(f) == 7
  3. We can iterate over it: 
    for n in f: print(n), list(f)
  4. We can reverse it: 
    for n in reversed(f): print(n), list(reversed(f))
  5. We can find things in it with in: 
    'three' in f == True

So, our

FakeList

 appears to behave like a list in almost all respects. But, how can we be sure that we have covered all the bases? Are we missing something? Is there a defined “interface” for “list-like objects” in Python?

Part 2: Abstract Base Classes

Abstract Base Classes, or ABCs, are a feature of Python that is not all that well known. There is some theory behind them, that they try to strike a balance between “static typing”, which in Python usually means using

isinstance

 a lot to determine if a value conforms with the type you are expecting, and “duck typing”, which usually means “don’t check the types of any value; instead interact with them as if they have the type you expect, and deal with the exceptions that will be raised if they don’t conform to your expected type’s interface”. ABCs introduce something that in the Python ecosystem is called “Goose typing”.

Long story short, Abstract Base Classes allow you to call

isinstance(obj, cls)

 and have it return

True

, when in fact obj is not an instance of

cls

 or one of its subclasses. Let’s see it in action:

class NotSized:
    def __len__(self, *args, **kwargs):
        pass

from collections.abc import Sized
isinstance(NotSized(), Sized)
# <<< True

You can write your own ABCs, and the theory behind why they are needed and how they work is interesting, but it is not what I want to talk about here. Because, apart from defying

isinstance

, they also have some functionality built-in. If you visit the documentation page of collections.abc, you will see the following section:

This tells us the following: If your class subclasses

Sequence

 and defines the

__getitem__

 and

__len__

 methods, then:

  1. calling isinstance(obj, Sequence) will return True and
  2. they will also have the other 5 methods: 
    __contains__

    ,

    __iter__

    ,

    __reversed__

    ,

    index

    and

    count

(You can verify the second statement by checking out the source code of Sequence; it’s neither big nor complicated)

The first statement is not really surprising, but it is important because it turns out that

isinstance(obj, Sequence) == True

 is the “official” way of saying that obj is a readable list-like object in Python.

What is interesting here is that, even without subclassing from Sequence, Python already gave

__contains__

,

__iter__

 and

__reversed__

 to our

FakeList

 class from Part 1. Lets put the last two mixin methods to the test:

f.index('two')
# AttributeError: 'FakeList' object has no attribute 'index'

f.count('two')
# AttributeError: 'FakeList' object has no attribute 'count'

We can fix this by subclassing FakeList from Sequence

+from collections.abc import Sequence
 
-class FakeList:
+class FakeList(Sequence):
     def __getitem__(self, index):
 ...
f.index('two')
# <<< 2

f.count('two')
# <<< 1

So the bottom line of all this is:

If you want to make something that can be “officially” considered a readable list-like object in Python, make it subclass Sequence and implement at least the 

__getitem__

 and 

__len__

 methods

The same conclusion holds true for all the ABCs listed in the documentation. For example, if you want to make a fully legitimate read-write list-like object, you would simply have to subclass from MutableSequence and implement the

__getitem__

,

__len__

,

__setitem__

,

__detitem__

 and insert
methods (the ones in the ‘Abstract methods’ column).

There is a note in the documentation which is interesting, so we are going to include it here verbatim:

Implementation note: Some of the mixin methods, such as 

__iter__()

, 

__reversed__()

 and 

index()

, make repeated calls to the underlying 

__getitem__()

 method. Consequently, if 

__getitem__()

 is implemented with constant access speed, the mixin methods will have linear performance; however, if the underlying method is linear (as it would be with a linked list), the mixins will have quadratic performance and will likely need to be overridden.

Part 3: Chainable Methods

We are going to shift topics away from list-like objects now. Don’t worry, everything will come together in the end. Let’s make another useless class.

class Counter:
    def __init__(self):
        self._count = 0

    def increment(self):
        self._count += 1

    def __repr__(self):
        return f"<Counter: {self._count}>"

c = Counter()
c.increment()
c.increment()
c.increment()
c
# <<< <Counter: 3>

Nothing surprising here.

It would be nice if we could make the

.increment

 calls chainable, i.e., if we could do:

c = Counter().increment().increment().increment()
c
# <<< <Counter: 3>

The easiest way to accomplish this is to have .increment() return the

Counter

 object itself:

 class Counter:
     def __init__(self):
         self._count = 0

     def increment(self):
         self._count += 1
+        return self

     def __repr__(self):
         return f"<Counter: {self._count}>"

However, this is not advisable. Here is an email from Guido van Rossum (the creator of Python) from 2003:

I'd like to explain once more why I'm so adamant that sort() shouldn't return
'self'.

This comes from a coding style (popular in various other languages, I believe
especially Lisp revels in it) where a series of side effects on a single object
can be chained like this:

    x.compress().chop(y).sort(z)

which would be the same as

    x.compress()
    x.chop(y)
    x.sort(z)

I find the chaining form a threat to readability; it requires that the reader
must be intimately familiar with each of the methods.  The second form makes it
clear that each of these calls acts on the same object, and so even if you
don't know the class and its methods very well, you can understand that the
second and third call are applied to x (and that all calls are made for their
side-effects), and not to something else.

I'd like to reserve chaining for operations that return new values, like string
processing operations:

    y = x.rstrip("\n").split(":").lower()

There are a few standard library modules that encourage chaining of side-effect
calls (pstat comes to mind).  There shouldn't be any new ones; pstat slipped
through my filter when it was weak.

--Guido van Rossum (home page:

Here is how I interpret this. If someone reads this snippet:

obj.do_something()

they will assume that

.do_something()
  • mutates obj in some way, and/or
  • has an interesting side-effect
  • probably returns 
    None

When they read this snippet:

obj2 = obj1.do_something()

they will assume that:

  • .do_something()

    does not change

    obj1

    in any way

  • obj2

    will have a new value, either a different type (eg a result status) or a slightly mutated copy of

    obj1

These assumptions break down when methods

return self
c1 = Counter().increment()
c2 = c1.increment()

c1
# <<< <Counter: 2>
c2
# <<< <Counter: 2>
c1 == c2
# <<< True

Someone not familiar with the implementation of

Counter

 would assume that

c1

 would hold the value

1

How do we fix this? My suggestion is: make the class’s initializer accept any optional arguments required to fully describe the instance’s state. Then, chainable methods will return a new instance with the appropriate, slightly changed, state.

 class Counter:
-    def __init__(self):
-        self._count = 0
+    def __init__(self, count=0):
+        self._count = count

     def increment(self):
-        self._count += 1
-        return self
+        return Counter(self._count + 1)

     def __repr__(self):
         return f"<Counter: {self._count}>"

Let’s try it out:

c1 = Counter().increment()
c2 = c1.increment()

c1
# <<< <Counter: 1>
c2
# <<< <Counter: 2>
c1 == c2
# <<< False

It might be a little better if we also do this:

 class Counter:
     def __init__(self, count=0):
         self._count = count

     def increment(self):
-        return Counter(self._count + 1)
+        return self.__class__(self._count + 1)

     def __repr__(self):
         return f"<Counter: {self._count}>"

so that

.increment()

 works for subclasses of

Counter

We essentially made the

Counter

 objects immutable, unless someone changes the “private”

_count

 attribute by hand.

Part 4: Bringing Everything Together

It’s now time to build something actually useful. Let’s consume an API and access the responses like lists. We are going to use the Transifex API (v3). Let’s start with a snippet:

import os
import requests

class TxCollection:
    HOST = "https://rest.api.transifex.com"

    def __init__(self, url):
        response = requests.get(
            self.HOST + url,
            headers={'Content-Type': "application/vnd.api+json",
                     'Authorization': f"Bearer {os.environ['API_TOKEN']}"},
        )
        response.raise_for_status()
        self.data = response.json()['data']
organizations = TxCollection("/organizations")
organizations.data[0]['attributes']['name']
# <<< 'diegobz'

Now let’s make this behave like a list:

-import os
+import os, reprlib, collections
 import requests
 
-class TxCollection:
+class TxCollection(collections.abc.Sequence):
     HOST = "https://rest.api.transifex.com"
 
     def __init__(self, url):
         response = requests.get(
             self.HOST + url,
             headers={'Content-Type': "application/vnd.api+json",
                      'Authorization': f"Bearer {os.environ['API_TOKEN']}"},
         )
         response.raise_for_status()
-        self.data = response.json()['data']
+        self._data = response.json()['data']
 
+    def __getitem__(self, index):
+        return self._data[index]
+
+    def __len__(self):
+        return len(self._data)
+
+    def __repr__(self):
+        result = ", ".join((reprlib.repr(item['id']) for item in self))
+        result = f"<TxCollection ({len(self)}): {result}>"
+        return result
organizations = TxCollection("/organizations")
organizations
# <<< <TxCollection (3): 'o:diegobz', 'o:kb_org', 'o:transifex'>

organizations[2]
# <<< {'id': 'o:transifex',
# ...  'type': 'organizations',
# ...  'attributes': {
# ...   'name': 'Transifex',
# ...   'slug': 'transifex',
# ...   'logo_url': 'https://txc-assets-775662142440-prod.s3.amazonaws.com/mugshots/435381b2e0.jpg',
# ...   'private': False},
# ...  'links': {'self': 'https://rest.api.transifex.com/organizations/o:transifex'}}

What is interesting here is that we know that our class is a legitimate readable list-like object because we fulfilled the requirements we set in Part 2: we subclassed from

collections.abc.Sequence

 and implemented the

__getitem__

 and

__len__

 methods.

Now, if you are familiar with Django querysets, you will know that you can apply filters to them and that their evaluation is applied lazily, i.e. evaluated on demand, after the filters have been set. Let’s try to apply this logic here, first by making our collections lazy:

 import os, reprlib, collections
 import requests
 
 class TxCollection(collections.abc.Sequence):
     HOST = "https://rest.api.transifex.com"
 
     def __init__(self, url):
+        self._url = url
+        self._data = None
 
+    def _evaluate(self):
+        if self._data is not None:
+            return
         response = requests.get(
-            self.HOST + url,
+            self.HOST + self._url,
             headers={'Content-Type': "application/vnd.api+json",
                      'Authorization': f"Bearer {os.environ['API_TOKEN']}"},
         )
         response.raise_for_status()
         self._data = response.json()['data']
 
     def __getitem__(self, index):
+        self._evaluate()
         return self._data[index]
 
     def __len__(self):
+        self._evaluate()
         return len(self._data)
 
     def __repr__(self):
         result = ", ".join((reprlib.repr(item['id']) for item in self))
         result = f"<TxCollection ({len(self)}): {result}>"
         return result
organizations = TxCollection("/organizations")
organizations
# <<< <TxCollection (3): 'o:diegobz', 'o:kb_org', 'o:transifex'>

Our lazy evaluation:

  • Will only be triggered when we try to access the collection like a list
  • Will abort early if the collection has already been evaluated

To drive point 1 home, I will point out that our

__repr__

 method (the one that was called when we typed

organizations <ENTER>

 into our python terminal) does not explicitly trigger an evaluation, but triggers it nevertheless. The for item in self part in its first line will start an iteration, which will call

__getitem__

 (as we saw in Part 1), which will trigger the evaluation. Even if it didn’t, the

len(self)

 part in the second line would also trigger the evaluation.

Playing with metaprogramming, which in this context means making things behave like things that they are not, can be tricky, dangerous and cause bugs, as anyone who has played with

__setattr__

 and ran into RecursionErrors can attest to. This is the beauty of the conclusion from Part 2: we want to make

TxCollection

 behave like a list and we know exactly which parts of the code trigger that behavior:

__getitem__

 and

__len__

. That’s the only parts we need to add our lazy evaluation to in order to be 100% confident that

TxCollection

 will properly behave like a readable list.

Now let’s apply filtering. We will intentionally do it the wrong way, by returning self, so that we can see the flaws outlined in Part 3 in the context of this example. Then we will fix it.

 class TxCollection(collections.abc.Sequence):
     HOST = "https://rest.api.transifex.com"

     def __init__(self, url):
         self._url = url
+        self._params = {}

         self._data = None

     def _evaluate(self):
         if self._data is not None:
             return
         response = requests.get(
             self.HOST + self._url,
+            params=self._params,
             headers={'Content-Type': "application/vnd.api+json",
                      'Authorization': f"Bearer {os.environ['API_TOKEN']}"},
         )
         response.raise_for_status()
         self._data = response.json()['data']
 
+    def filter(self, **filters):
+        self._params.update({f'filter[{key}]': value
+                             for key, value in filters.items()})
+        return self

     # def __getitem__, __len__, __repr__

Let’s take this out for a spin:

TxCollection("/resource_translations").\
    filter(resource="o:kb_org:p:kb1:r:fileless", language="l:el")
# <<< <TxCollection (3): 'o:kb_org:p:k...72e4fdb0:l:el',
# ...                    'o:kb_org:p:k...e877d7ee:l:el',
# ...                    'o:kb_org:p:k...ed953f8f:l:el'>

(Note: There are some Transifex-API-v3-specific things here, like how filtering is applied and what the IDs of the objects look like, that you don’t have to worry about. If you are interested, you can check out the documentation)

And now let’s demonstrate the flaw we outlined in Part 3:

c1 = TxCollection("/resource_translations").\
    filter(resource="o:kb_org:p:kb1:r:fileless", language="l:el")
c2 = c1.filter(translated="true")

c1
# <<< <TxCollection (1): 'o:kb_org:p:k...72e4fdb0:l:el'>
c2
# <<< <TxCollection (1): 'o:kb_org:p:k...72e4fdb0:l:el'>
c1 == c2
# <<< True

We know from our previous run that

c1

 should have a size of 3, but it got overwritten when we applied

.filter()

 to it.

Also,

c1 = TxCollection("/resource_translations").\
    filter(resource="o:kb_org:p:kb1:r:fileless", language="l:el")
_ = list(c1)
c2 = c1.filter(translated="true")

c1
# <<< <TxCollection (3): 'o:kb_org:p:k...72e4fdb0:l:el',
# ...                    'o:kb_org:p:k...e877d7ee:l:el',
# ...                    'o:kb_org:p:k...ed953f8f:l:el'>
c2
# <<< <TxCollection (3): 'o:kb_org:p:k...72e4fdb0:l:el',
# ...                    'o:kb_org:p:k...e877d7ee:l:el',
# ...                    'o:kb_org:p:k...ed953f8f:l:el'>
c1 == c2
# <<< True

We forced an evaluation before we applied the second filter (with

_ = list(c1)

), so the second filter was ignored, in both

c1

 and

c2

To fix this, we will do the same thing we did in Part 3: we will add optional arguments to the initializer that describe the whole state of a

TxCollection

 object and have

.filter()

 return a slightly mutated copy of self.

 class TxCollection(collections.abc.Sequence):
     HOST = "https://rest.api.transifex.com"
 
-    def __init__(self, url):
+    def __init__(self, url, params=None):
+        if params is None:
+            params = {}

         self._url = url
-        self._params = {}
+        self._params = params

         self._data = None

     # def _evaluate
 
-    def filter(self, **filters):
-        self._params.update({f'filter[{key}]': value
-                             for key, value in filters.items()})
-        return self
+    def filter(self, **filters):
+        params = dict(self._params)  # Make a copy
+        params.update({f'filter[{key}]': value
+                       for key, value in filters.items()})
+        return self.__class__(self._url, params)

     # def __getitem__, __len__, __repr__

(Note: we didn’t set 

params={}

 as the default value in the initializer because you shouldn’t use mutable default arguments)

c1 = TxCollection("/resource_translations").\
    filter(resource="o:kb_org:p:kb1:r:fileless", language="l:el")
c2 = c1.filter(translated="true")

c1
# <<< <TxCollection (3): 'o:kb_org:p:k...72e4fdb0:l:el',
# ...                    'o:kb_org:p:k...e877d7ee:l:el',
# ...                    'o:kb_org:p:k...ed953f8f:l:el'>
c2
# <<< <TxCollection (1): 'o:kb_org:p:k...72e4fdb0:l:el'>
c1 == c2
# <<< False

Works like a charm!

We concluded Part 3 by saying that the class we made creates immutable objects, which is why it is safe to use chainable methods on them. What is interesting here is that

TxCollection

 objects are not immutable. So, how do we ensure that implementing chainable methods is safe? The answer is that the state of a

TxCollection

 consists of two parts:

  • The 
    _url

    and

    _params

    attributes that are immutable.

  • The 
    _data

    attribute which is dynamic. But:
    it will only be evaluated once and it has a deterministic relationship with the immutable parts. The only way for

    _data

    to be evaluated differently is to change

    _url

    and

    _params

    , which can only happen if we make a mutated copy of the original object via

    .filter()

Conclusion

I hope this has been interesting. You can write powerful and expressive code with what is explained here, hopefully without introducing bugs

This article has been published from a wire agency feed without modifications to the text. Only the headline has been changed.

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