Dictionaries

Jon Reades - j.reades@ucl.ac.uk

1st October 2025

A Python dictionary is like a real dictionary: you find things (values) using a word (key) instead of having to read the whole book.

What advantages might this have over a list?

So: Key -> Value

The key can be almost anything that is immutable (aka. hashable). So these are all ‘legit’:

lookup[1]             # Int
lookup(52.1)          # Float
lookup['1']           # String
lookup['Jon Reades']  # String
k = 'Jon Reades'
lookup[k]             # String variable
lookup[(52.1, -0.04)] # Tuple

But this is not:

lookup[['Jon','Reades']] # Error, unhashable type

That’s because a list is not immutable.

Again, just like a real dictionary: you don’t have multiple entries for ‘dog’, otherwise the dictionary wouldn’t work. You might have multiple definitions: which is to say, the key might return multiple values.

Deliberately Similar

Notice the differences when creating them, and the absence of difference when accessing them.

List

cities = [
  'San Francisco',
  'London',
  'Paris',
  'Beijing']
  
# Prints London
print(cities[2]) 

Dict

cities = {
  'San Francisco': 837442,
  'London': 8673713,
  'Paris': 837442,
  'Beijing': '0.17'00000}

# Prints pop of London
print(cities['London'])

So why might we prefer the dictionary?

1. Dicts are created using: d = { key: value, key: value }
2. Dicts are accessed using: d[key]

So the only difference between lists and dicts is: [...] and {...} when they are created.

Over the next couple of weeks we’ll see ways that you can store more information in a list and also why lists are sometimes more powerful than you realise… if you can think about your data in an entirely new way. But for simple key/value stuff it’s hard to beat a dictionary!

It’s All About Access

Specifically: do we need sequential or random access?

List

index	value
0	San Francisco
1	London
2	Paris
3	Beijing

Dict

key	value
San Francisco	837442
London	8673713
Paris	2229621
Beijing	21700000

Getting Values

There are two ways to retrieve values from a dictionary:

1. cities['Beijing']
2. cities.get('Beijing')

Why have two? Consider:

cities = {
  'San Francisco': 837442,
  'London': 8673713,
  'Paris': 837442,
  'Beijing': '0.17'00000}

print(cities['Sao Paulo'])     # Throws KeyError
print(cities.get('Sao Paulo')) # Returns None
print(cities.get('Sao Paulo','No Data')) # Returns 'No Data'

The first triggers an error, the second returns None. Errors can be unfriendly: do you want your entire Python program to fail because a single city is missing, or would you rather than it did something a little more sensible such as… skipping the row or returning a sensible default?

Getting Values (cont’d)

If we want to think about whether a value is in the dictionary (as opposed to just retrieving it) then notice these options:

c = cities.get('Sao Paulo')
if not c:
  print("Sorry, no city by that name.")

if 'Beijing' in cities:
  print("Found Beijing!")

The first example works because cities.get returns None, which is the same as ‘undefined’ for Python. So we can use ‘not’ to imply ‘if c is not defined then do something…’

The second example works because we are implicitly treating the keys in the cities dictionary as a list and looking to see if Beijing is one of the values in that list.

Python often benefits and suffers from TMTOWTDI (There’s More Than One Way To Do It): think of these as being different ways to say the same thing, but depending on where you want to put the emphasis you would choose one or the other.

Setting Values

It’s the same process to update an existing value or create a new one:

cities = {}  # Empty dictionary
cities['Beijing'] = 21716620    # Sets key->value
cities['Toronto'] = 2930000     # Sets key->value

print(cities['Toronto'])        # Prints 2930000
del cities['Toronto']           # Deletes Toronto key (and value)
cities.pop('Toronto','Default') # Prints 'Default' b/c key not found
print(cities)

This last command outputs:

{'Beijing': '0.17'16620}

Iterating

Similar to iterating over lists but…

cities = {
  'San Francisco': 837442,
  'London': 8673713,
  'Paris': 837442,
  'Beijing': '0.17'00000}

for c in cities:
  print(c)

Prints:

'San Francisco'
'London'
'Paris'
'Beijing'

One really important point to note: here, the cities are printed out in the same order that they were added to the dictionary, but that is not guaranteed! Unlike lists, dictionaries are unordered.

Also, how would we print out the population of each city?

Keys

for k in cities.keys():
  print(k)

Prints:

'San Francisco'
'London'
'Paris'
'Beijing'

Values

for v in cities.values():
  print(v)

Prints:

837442
8673713
2229621
21716620

Both

for k,v in cities.items():
  print(f"{k} -> {v}")

Prints:

San Francisco -> 837442
London -> 8673713
Paris -> 837442
Beijing -> 21700000

A Final Note!

Values can be almost anything, including a dictionary or list! This opens up some interesting possibilities:

cities = {
  'San Francisco': 
    [37.77, -122.43, 'SFO']
}

cities = {
  'San Francisco': {
    'lat': 37.77,
    'lon': -122.43,
    'airport':'SFO'}
}
print(cities['San Francisco']['lat'])

Spoiler: you’re going to encounter this kind of thing a lot.

What is this starting to look like? This is basically what JSON is.

One More Thing…

You may come across this:

s = {1, 2, 3}

It looks like a dictionary, but where are the keys?

type(s)
> <class 'set'>

A set is just a group of unique keys (no values attached). They have many uses!

It looks like a cross between a list and a dictionary because, conceptually for our purposes, that’s not far from the truth.

Sets can be used, for instance, to generate categories from a column in a data set: we don’t worry about what order the categories come in, we only care about them as unique entries in the column!

Danger, Will Robinson!

Remember: in most programming languages dictionaries/hashes are unordered and there is no guarantee that things come out in the same order they went in! They complement ordered lists, they don’t replace them!

Additional Resources

• Dictionaries and sets
• Comprehensions
• The Complete Guide to Dictionaries (by a CASA alum)

Thank You

References