Storing information

Every program is really just moving and transforming values — numbers, text, true/false flags. A variable is simply a name you attach to a value so you can refer to it later. In Python you don't declare types; you assign, and Python figures out the type for you:

name = "Ada"          # str   — text
age = 36              # int   — whole number
ratio = 0.75          # float — decimal
is_active = True      # bool  — True or False

Read = as "gets" — "name gets 'Ada'." You can reassign freely, and you can check a value's type any time with type(age).

The core types

Four basic types carry most of the load:

• str — text, in quotes. Join with +, format with f-strings: f"Hi {name}".
• int and float — numbers, whole and decimal, with the usual + - * /.
• bool — True or False, the basis of every decision.

Collections you'll use constantly

Single values only get you so far; real data comes in groups. Three collections appear everywhere in AI work:

• list — ordered and changeable: [1, 2, 3]. Add with .append(), grab by position with nums[0].
• dict — key/value pairs: {"name": "Ada", "age": 36}. Look things up by name: person["name"]. Perfect for structured records.
• tuple — ordered but fixed: (lat, lon). Use it for values that belong together and shouldn't change.

Get comfortable with lists and dicts above all — almost all data work is moving information in and out of them, and every dataset you load becomes some combination of the two.

Variables name values; collections group them. Master list (a row of things) and dict (labeled things) and you can represent almost any data you'll meet.

Try this: In a notebook, make a dict describing yourself — name, age, and a list of hobbies — then print one field and append a new hobby to the list. Storing and updating structured data like this is most of what code does.