Table 4-1 previews Python’s built-in object types and some of the syntax used to code their literals—that is, the expressions that generate these objects.^[12] Some of these types will probably seem familiar if you’ve used other languages; for instance, numbers and strings represent numeric and textual values, respectively, and file objects provide an interface for processing real files stored on your computer.

To some readers, though, the object types in Table 4-1 may be more general and powerful than what you are accustomed to. For instance, you’ll find that lists and dictionaries alone are powerful data representation tools that obviate most of the work you do to support collections and searching in lower-level languages. In short, lists provide ordered collections of other objects, while dictionaries store objects by key; both lists and dictionaries may be nested, can grow and shrink on demand, and may contain objects of any type.

Also shown in Table 4-1, program units such as functions, modules, and classes—which we’ll meet in later parts of this book—are objects in Python too; they are created with statements and expressions such as def, class, import, and lambda and may be passed around scripts freely, stored within other objects, and so on. Python also provides a set of implementation-related types such as compiled code objects, which are generally of interest to tool builders more than application developers; we’ll explore these in later parts too, though in less depth due to their specialized roles.

Despite its title, Table 4-1 isn’t really complete, because everything we process in Python programs is a kind of object. For instance, when we perform text pattern matching in Python, we create pattern objects, and when we perform network scripting, we use socket objects. These other kinds of objects are generally created by importing and using functions in library modules—for example, in the re and socket modules for patterns and sockets—and have behavior all their own.

We usually call the other object types in Table 4-1 core data types, though, because they are effectively built into the Python language—that is, there is specific expression syntax for generating most of them. For instance, when you run the following code with characters surrounded by quotes:

>>> 'spam'

you are, technically speaking, running a literal expression that generates and returns a new string object. There is specific Python language syntax to make this object. Similarly, an expression wrapped in square brackets makes a list, one in curly braces makes a dictionary, and so on. Even though, as we’ll see, there are no type declarations in Python, the syntax of the expressions you run determines the types of objects you create and use. In fact, object-generation expressions like those in Table 4-1 are generally where types originate in the Python language.

Just as importantly, once you create an object, you bind its operation set for all time—you can perform only string operations on a string and list operations on a list. In formal terms, this means that Python is dynamically typed, a model that keeps track of types for you automatically instead of requiring declaration code, but it is also strongly typed, a constraint that means you can perform on an object only operations that are valid for its type.

We’ll study each of the object types in Table 4-1 in detail in upcoming chapters. Before digging into the details, though, let’s begin by taking a quick look at Python’s core objects in action. The rest of this chapter provides a preview of the operations we’ll explore in more depth in the chapters that follow. Don’t expect to find the full story here—the goal of this chapter is just to whet your appetite and introduce some key ideas. Still, the best way to get started is to get started, so let’s jump right into some real code.