Learning Python - Mark Lutz [130]
'toast'
>>> L
['spam', 'ham']
See other documentation sources or experiment with these calls interactively on your own to learn more about list methods.
Other common list operations
Because lists are mutable, you can use the del statement to delete an item or section in-place:
>>> L
['SPAM!', 'eat', 'more', 'please']
>>> del L[0] # Delete one item
>>> L
['eat', 'more', 'please']
>>> del L[1:] # Delete an entire section
>>> L # Same as L[1:] = []
['eat']
Because slice assignment is a deletion plus an insertion, you can also delete a section of a list by assigning an empty list to a slice (L[i:j]=[]); Python deletes the slice named on the left, and then inserts nothing. Assigning an empty list to an index, on the other hand, just stores a reference to the empty list in the specified slot, rather than deleting it:
>>> L = ['Already', 'got', 'one']
>>> L[1:] = []
>>> L
['Already']
>>> L[0] = []
>>> L
[[]]
Although all the operations just discussed are typical, there are additional list methods and operations not illustrated here (including methods for inserting and searching). For a comprehensive and up-to-date list of type tools, you should always consult Python’s manuals, Python’s dir and help functions (which we first met in Chapter 4), or one of the reference texts mentioned in the Preface.
I’d also like to remind you one more time that all the in-place change operations discussed here work only for mutable objects: they won’t work on strings (or tuples, discussed in Chapter 9), no matter how hard you try. Mutability is an inherent property of each object type.
* * *
[22] This description needs elaboration when the value and the slice being assigned overlap: L[2:5]=L[3:6], for instance, works fine because the value to be inserted is fetched before the deletion happens on the left.
[23] Unlike + concatenation, append doesn’t have to generate new objects, so it’s usually faster. You can also mimic append with clever slice assignments: L[len(L):]=[X] is like L.append(X), and L[:0]=[X] is like appending at the front of a list. Both delete an empty slice and insert X, changing L in-place quickly, like append.
Dictionaries
Apart from lists, dictionaries are perhaps the most flexible built-in data type in Python. If you think of lists as ordered collections of objects, you can think of dictionaries as unordered collections; the chief distinction is that in dictionaries, items are stored and fetched by key, instead of by positional offset.
Being a built-in type, dictionaries can replace many of the searching algorithms and data structures you might have to implement manually in lower-level languages—indexing a dictionary is a very fast search operation. Dictionaries also sometimes do the work of records and symbol tables used in other languages, can represent sparse (mostly empty) data structures, and much more. Here’s a rundown of their main properties. Python dictionaries are:
Accessed by key, not offset
Dictionaries are sometimes called associative arrays or hashes. They associate a set of values with keys, so you can fetch an item out of a dictionary using the key under which you originally stored it. You use the same indexing operation to get components in a dictionary as you do in a list, but the index takes the form of a key, not a relative offset.
Unordered collections of arbitrary objects
Unlike in a list, items stored in a dictionary aren’t kept in any particular order; in fact, Python randomizes their left-to-right order to provide quick lookup. Keys provide the symbolic (not physical) locations of items in a dictionary.
Variable-length, heterogeneous, and arbitrarily nestable
Like lists, dictionaries can grow and shrink in-place (without new copies being made), they can contain objects of any type, and they support nesting to any depth (they can contain lists, other dictionaries, and so on).
Of the category “mutable mapping”
Dictionaries can be changed in-place by assigning to indexes (they are mutable), but they don’t support