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Types Share Operation Sets by Categories

As you’ve learned, strings are immutable sequences: they cannot be changed in-place (the immutable part), and they are positionally ordered collections that are accessed by offset (the sequence part). Now, it so happens that all the sequences we’ll study in this part of the book respond to the same sequence operations shown in this chapter at work on strings—concatenation, indexing, iteration, and so on. More formally, there are three major type (and operation) categories in Python:

Numbers (integer, floating-point, decimal, fraction, others)

Support addition, multiplication, etc.

Sequences (strings, lists, tuples)

Support indexing, slicing, concatenation, etc.

Mappings (dictionaries)

Support indexing by key, etc.

Sets are something of a category unto themselves (they don’t map keys to values and are not positionally ordered sequences), and we haven’t yet explored mappings on our in-depth tour (dictionaries are discussed in the next chapter). However, many of the other types we will encounter will be similar to numbers and strings. 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 way on any kind of sequence, including strings, lists, tuples, and some user-defined object types. The only difference is that the new result object you get back is of 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 flavor of the task to perform.