5.5 General Type Categories

Now that we've seen the first collection object, the string, let's pause to define a few general type concepts that will apply to most of the types from here on. In regard to built-in types, it turns out that operations work the same for all types in a category, so we only need to define most ideas once. We've only seen numbers and strings so far; but because they are representative of two of the three major type categories in Python, you already know more about other types than you think.

5.5.1 Types Share Operation Sets by Categories

Strings are immutable sequences: they cannot be changed in place (the immutable part), and are positionally-ordered collections that are accessed by offsets (the sequence part). Now, it so happens that all the sequences seen in this part of the book respond to the same sequence operations shown at work on strings—concatenation, indexing, iteration, and so on. More formally, there are three type (and operation) categories in Python:

Numbers

Support addition, multiplication, etc.

Sequences

Support indexing, slicing, concatenation, etc.

Mappings

Support indexing by key, etc.

We haven't seen mappings yet (dictionaries are discussed in the next chapter), but other types are going to be mostly more of the same. For example, for any sequence objects X and Y:

• X + Y makes a new sequence object with the contents of both operands.

• X * N makes a new sequence object with N copies of the sequence operand X.

In other words, these operations work the same on any kind of sequence—strings, lists, tuples, and some user-defined object types. The only difference is that you get back a new result object that is the same type as the operands X and Y—if you concatenate lists, you get back a new list, not a string. Indexing, slicing, and other sequence operations work the same on all sequences too; the type of the objects being processed tells Python which task to perform.

5.5.2 Mutable Types Can Be Changed in-Place

The immutable classification is an important constraint to know yet it tends to trip up new users. If an object type is immutable, you cannot change its value in-place; Python raises an error if you try. Instead, run code to make a new object for a new value. Generally, immutable types give some degree of integrity, by guaranteeing that an object won't be changed by another part of a program. You'll see why this matters when shared object references are discussed in Chapter 7.