Learning Python - Mark Lutz [96]
As you’ll see here and later in this book, in Python, we do not declare the specific types of the objects our scripts use. In fact, programs should not even care about specific types; in exchange, they are naturally applicable in more contexts than we can sometimes even plan ahead for. Because dynamic typing is the root of this flexibility, let’s take a brief look at the model here.
The Case of the Missing Declaration Statements
If you have a background in compiled or statically typed languages like C, C++, or Java, you might find yourself a bit perplexed at this point in the book. So far, we’ve been using variables without declaring their existence or their types, and it somehow works. When we type a = 3 in an interactive session or program file, for instance, how does Python know that a should stand for an integer? For that matter, how does Python know what a is at all?
Once you start asking such questions, you’ve crossed over into the domain of Python’s dynamic typing model. In Python, types are determined automatically at runtime, not in response to declarations in your code. This means that you never declare variables ahead of time (a concept that is perhaps simpler to grasp if you keep in mind that it all boils down to variables, objects, and the links between them).
Variables, Objects, and References
As you’ve seen in many of the examples used so far in this book, when you run an assignment statement such as a = 3 in Python, it works even if you’ve never told Python to use the name a as a variable, or that a should stand for an integer-type object. In the Python language, this all pans out in a very natural way, as follows:
Variable creation
A variable (i.e., name), like a, is created when your code first assigns it a value. Future assignments change the value of the already created name. Technically, Python detects some names before your code runs, but you can think of it as though initial assignments make variables.
Variable types
A variable never has any type information or constraints associated with it. The notion of type lives with objects, not names. Variables are generic in nature; they always simply refer to a particular object at a particular point in time.
Variable use
When a variable appears in an expression, it is immediately replaced with the object that it currently refers to, whatever that may be. Further, all variables must be explicitly assigned before they can be used; referencing unassigned variables results in errors.
In sum, variables are created when assigned, can reference any type of object, and must be assigned before they are referenced. This means that you never need to declare names used by your script, but you must initialize names before you can update them; counters, for example, must be initialized to zero before you can add to them.
This dynamic typing model is strikingly different from the typing model of traditional languages. When you are first starting out, the model is usually easier to understand if you keep clear the distinction between names and objects. For example, when we say this:
>>> a = 3
at least conceptually, Python will perform three distinct steps to carry out the request. These steps reflect the operation of all assignments in the Python language:
Create an object to represent the value 3.
Create the variable a, if it does not yet exist.
Link the variable a to the new object 3.
The net result will be a structure inside Python that resembles Figure 6-1. As sketched, variables and objects are stored in different parts of memory and are associated by links (the link is shown as a pointer in the figure). Variables always link to objects and never to other variables, but larger objects may link to other objects (for instance, a list object has links to the objects it contains).
Figure 6-1. Names and objects after running the assignment a = 3. Variable a becomes a reference to the object 3. Internally, the variable is really a pointer to the object’s memory space created by running the literal expression