Python Dictionary

Of all the built-in Python data types, the dictionary is easily the most interesting one. It’s the only standard mapping type, and it is the backbone of every Python object.

A dictionary maps keys to values. Keys need to be hashable objects, while values can be of any arbitrary type. Dictionaries are mutable objects. There are quite a few different ways to create a dictionary, so let me give you a simple example of how to create a dictionary equal to {‘A’: 1, ‘Z’: -1} in five different ways:

>>> a = dict(A=1, Z=-1)
>>> b = {'A': 1, 'Z': -1}
>>> c = dict(zip(['A', 'Z'], [1, -1]))
>>> d = dict([('A', 1), ('Z', -1)])
>>> e = dict({'Z': -1, 'A': 1})
>>> a == b == c == d == e  # are they all the same?
True  # They are indeed

Have you noticed those double equals? The assignment is done with one equal, while to check whether an object is the same as another one (or five in one go, in this case), we use double equals. There is also another way to compare objects, which involves the is operator and checks whether the two objects are the same (if they have the same ID, not just the value), but unless you have a good reason to use it, you should use the double equals instead. In the preceding code, I also used one nice function: zip. It is named after the real-life zip, which glues together two things taking one element from each at a time.

Let me show you an example:

>>> list(zip(['h', 'e', 'l', 'l', 'o'], [1, 2, 3, 4, 5]))
[('h', 1), ('e', 2), ('l', 3), ('l', 4), ('o', 5)]
>>> list(zip('hello', range(1, 6)))  # equivalent, more Pythonic
[('h', 1), ('e', 2), ('l', 3), ('l', 4), ('o', 5)]

In the preceding example, I have created the same list in two different ways, one more explicit, and the other a little bit more Pythonic. Forget for a moment that I had to wrap the list constructor around the zip call (the reason is that zip returns an iterator, not a list, so if I want to see the result I need to exhaust that iterator into something—a list in this case), and concentrate on the result.

See how zip has coupled the first elements of its two arguments together, then the second ones, then the third ones, and so on and so forth? Take a look at your pants (or at your purse, if you’re a lady) and you’ll see the same behavior in your actual zip. But let’s go back to dictionaries and see how many wonderful methods they expose for allowing us to manipulate them as we want.

Let’s start with the basic operations:

>>> d = {}
>>> d['a'] = 1  # let's set a couple of (key, value) pairs
>>> d['b'] = 2
>>> len(d)  # how many pairs?
2
>>> d['a']  # what is the value of 'a'?
1
>>> d  # how does `d` look now?
{'a': 1, 'b': 2}
>>> del d['a']  # let's remove `a`
>>> d
{'b': 2}
>>> d['c'] = 3  # let's add 'c': 3
>>> 'c' in d  # membership is checked against the keys
True
>>> 3 in d  # not the values
False
>>> 'e' in d
False
>>> d.clear()  # let's clean everything from this dictionary
>>> d
{}

Watch as the access keys of a dictionary is done through  square bracket regardless of the type of operation.

Notice how accessing keys of a dictionary, regardless of the type of operation we’re performing, is done through square brackets. Do you remember strings, lists, and tuples? We were accessing elements at some position through square brackets as well, which is yet another example of Python’s consistency.

Let’s see now three special objects called dictionary views: keys, values, and items. These objects provide a dynamic view of the dictionary entries and they change when the dictionary changes. keys() returns all the keys in the dictionary, values() returns all the values in the dictionary, and items() returns all the (key, value) pairs in the dictionary.

According to the Python documentation: “Keys and values are iterated over in an arbitrary order which is non-random, varies across Python implementations, and depends on the dictionary’s history of insertions and deletions. If keys, values and items views are iterated over with no intervening modifications to the dictionary, the order of items will directly correspond.”

let’s put all this down into code:

>>> d = dict(zip('hello', range(5)))
>>> d
{'h': 0, 'e': 1, 'l': 3, 'o': 4}
>>> d.keys()
dict_keys(['h', 'e', 'l', 'o'])
>>> d.values()
dict_values([0, 1, 3, 4])
>>> d.items()
dict_items([('h', 0), ('e', 1), ('l', 3), ('o', 4)])
>>> 3 in d.values()
True
>>> ('o', 4) in d.items()
True

There are a few things to notice in the preceding code. First, notice how we’re creating a dictionary by iterating over the zipped version of the string ‘hello’ and the list [0, 1, 2, 3, 4]. The string ‘hello’ has two ‘l’characters inside, and they are paired up with the values 2 and 3 by the zip function. Notice how in the dictionary, the second occurrence of the ‘l’ key (the one with value 3), overwrites the first one (the one with value 2). Another thing to notice is that when asking for any view, the original order is now preserved, while before Version 3.6 there was no guarantee of that.

We’ll see how these views are fundamental tools when we talk about iterating over collections. Let’s take a look now at some other methods exposed by Python’s dictionaries; there’s plenty of them and they are very useful:

>>> d
{'e': 1, 'h': 0, 'o': 4, 'l': 3}
>>> d.popitem()  # removes a random item (useful in algorithms)
('o', 4)
>>> d
{'h': 0, 'e': 1, 'l': 3}
>>> d.pop('l')  # remove item with key `l`
3
>>> d.pop('not-a-key')  # remove a key not in dictionary: KeyError
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
KeyError: 'not-a-key'
>>> d.pop('not-a-key', 'default-value')  # with a default value?
'default-value'  # we get the default value
>>> d.update({'another': 'value'})  # we can update dict this way
>>> d.update(a=13)  # or this way (like a function call)
>>> d
{'h': 0, 'e': 1, 'another': 'value', 'a': 13}
>>> d.get('a')  # same as d['a'] but if key is missing no KeyError
13
>>> d.get('a', 177)  # default value used if key is missing
13
>>> d.get('b', 177)  # like in this case
177
>>> d.get('b')  # key is not there, so None is returned

All these methods are quite simple to understand, but it’s worth talking about that None, for a moment. Every function in Python returns None unless the return statement is explicitly used to return something else, but we’ll see this when we explore functions. None is frequently used to represent the absence of a value, and it is quite commonly used as a default value for arguments in the function declaration. Some inexperienced coders sometimes write code that returns either False or None.

Both False and None evaluate to False in a Boolean context so it may seem there is not much difference between them. But actually, I would argue there is quite an important difference: False means that we have information, and the information we have is False. None means no information. And no information is very different from the information that is False. In layman’s terms, if you ask your mechanic,  Is my car ready?, there is a big difference between the answer, No, it’s not (False) and, I have no idea (None).

One last method I really like about dictionaries is set to default. It behaves like getting but also sets the key with the given value if it is not there.

Let’s see an example:

>>> d = {}
>>> d.setdefault('a', 1)  # 'a' is missing, we get default value
1
>>> d
{'a': 1}  # also, the key/value pair ('a', 1) has now been added
>>> d.setdefault('a', 5)  # let's try to override the value
1
>>> d
{'a': 1}  # no override, as expected

So, we're now at the end of this tour. Test your knowledge about dictionaries by trying to foresee what looks like after this line:
>> d = {}
>>> d.setdefault('a', {}).setdefault('b', []).append(1)

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