Dictionaries and sets

When you need to look something up by a name rather than a position, you want a dictionary. When you need uniqueness or fast "is this in here?" checks, you want a set. These are the everyday structures from the fundamentals lesson, in Python form.

Dictionaries: key → value

A dict maps keys to values, written with {key: value}:

person = {"name": "Ada", "born": 1815}
person["name"]            # "Ada"   (look up by key)
person["role"] = "pioneer"# add a new key
person.get("age", 0)      # 0       (default if key is missing — no error)
"name" in person          # True

Looking up by key is effectively instant no matter how big the dict gets — that's the whole reason to use one over scanning a list.

Iterating a dict

for key, value in person.items():
    print(key, "->", value)

person.keys() and person.values() give you each side alone.

Sets: unique items

A set holds unique values and answers membership fast:

seen = {1, 2, 2, 3}     # {1, 2, 3}  — duplicates collapse
3 in seen               # True
seen.add(4)
unique = set([1, 1, 2]) # {1, 2}     — dedupe a list

Use a set to remove duplicates or to check membership against a large collection cheaply — exactly the "let the operations choose the structure" idea.

Where to go next

You now have Python's core data types. Next: errors and exceptions — handling things going wrong without crashing.