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TestMaker Script
Guide Table
of Contents Published: October
21, 2002 |
Data Structures
|
TestMaker Script
Guide Table
of Contents Published: October
21, 2002 |
This chapter describes some things you've learned about already in more detail, and adds some new things as well.
The list data type has some more methods. Here are all of the methods of list objects:
append(x)a[len(a):] =
[x].
extend(L)a[len(a):] =
L.
insert(i,
x)a.insert(0, x) inserts at the front of the list,
and a.insert(len(a), x) is equivalent to
a.append(x).
remove(x)x. It is an error if there is no such
item.
pop([i])a.pop() returns the last item in the list. The item
is also removed from the list.
index(x)x. It is an error if there is no
such item.
count(x)x
appears in the list.
sort()
reverse()
An example that uses most of the list methods:
>>> a = [66.6, 333, 333, 1, 1234.5]
>>> print a.count(333), a.count(66.6), a.count('x')
2 1 0
>>> a.insert(2, -1)
>>> a.append(333)
>>> a
[66.6, 333, -1, 333, 1, 1234.5, 333]
>>> a.index(333)
1
>>> a.remove(333)
>>> a
[66.6, -1, 333, 1, 1234.5, 333]
>>> a.reverse()
>>> a
[333, 1234.5, 1, 333, -1, 66.6]
>>> a.sort()
>>> a
[-1, 1, 66.6, 333, 333, 1234.5]
The list methods make it very easy to use a list as a stack, where the last element added is the first element retrieved (``last-in, first-out''). To add an item to the top of the stack, use append(). To retrieve an item from the top of the stack, use pop() without an explicit index. For example:
>>> stack = [3, 4, 5] >>> stack.append(6) >>> stack.append(7) >>> stack [3, 4, 5, 6, 7] >>> stack.pop() 7 >>> stack [3, 4, 5, 6] >>> stack.pop() 6 >>> stack.pop() 5 >>> stack [3, 4]
You can also use a list conveniently as a
queue, where the first element added is the first element retrieved
(``first-in, first-out''). To add an item to the back of the queue,
use append(). To retrieve an item from the front of the
queue, use pop() with 0 as the index. For
example:
>>> queue = ["Eric", "John", "Michael"]
>>> queue.append("Terry") # Terry arrives
>>> queue.append("Graham") # Graham arrives
>>> queue.pop(0)
'Eric'
>>> queue.pop(0)
'John'
>>> queue
['Michael', 'Terry', 'Graham']
There are three built-in functions that are very useful when used with lists: filter(), map(), and reduce().
"filter(function,
sequence)" returns a sequence (of the same type, if
possible) consisting of those items from the sequence for which
function(item) is true. For
example, to compute some primes:
>>> def f(x): return x % 2 != 0 and x % 3 != 0 ... >>> filter(f, range(2, 25)) [5, 7, 11, 13, 17, 19, 23]
"map(function,
sequence)" calls
function(item) for each of the
sequence's items and returns a list of the return values. For
example, to compute some cubes:
>>> def cube(x): return x*x*x ... >>> map(cube, range(1, 11)) [1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]
More than one sequence may be passed; the
function must then have as many arguments as there are sequences and
is called with the corresponding item from each sequence (or
None if some sequence is shorter than another). If
None is passed for the function, a function returning
its argument(s) is substituted.
Combining these two special cases, we see that "map(None, list1, list2)" is a convenient way of turning a pair of lists into a list of pairs. For example:
>>> seq = range(8) >>> def square(x): return x*x ... >>> map(None, seq, map(square, seq)) [(0, 0), (1, 1), (2, 4), (3, 9), (4, 16), (5, 25), (6, 36), (7, 49)]
"reduce(func, sequence)" returns a single value constructed by calling the binary function func on the first two items of the sequence, then on the result and the next item, and so on. For example, to compute the sum of the numbers 1 through 10:
>>> def add(x,y): return x+y ... >>> reduce(add, range(1, 11)) 55
If there's only one item in the sequence, its value is returned; if the sequence is empty, an exception is raised.
A third argument can be passed to indicate the starting value. In this case the starting value is returned for an empty sequence, and the function is first applied to the starting value and the first sequence item, then to the result and the next item, and so on. For example,
>>> def sum(seq): ... def add(x,y): return x+y ... return reduce(add, seq, 0) ... >>> sum(range(1, 11)) 55 >>> sum([]) 0
List comprehensions provide a concise way to create lists without resorting to use of map(), filter() and/or lambda. The resulting list definition tends often to be clearer than lists built using those constructs. Each list comprehension consists of an expression following by a for clause, then zero or more for or if clauses. The result will be a list resulting from evaluating the expression in the context of the for and if clauses which follow it. If the expression would evaluate to a tuple, it must be parenthesized.
>>> freshfruit = [' banana', ' loganberry ', 'passion fruit ']
>>> [weapon.strip() for weapon in freshfruit]
['banana', 'loganberry', 'passion fruit']
>>> vec = [2, 4, 6]
>>> [3*x for x in vec]
[6, 12, 18]
>>> [3*x for x in vec if x > 3]
[12, 18]
>>> [3*x for x in vec if x < 2]
[]
>>> [{x: x**2} for x in vec]
[{2: 4}, {4: 16}, {6: 36}]
>>> [[x,x**2] for x in vec]
[[2, 4], [4, 16], [6, 36]]
>>> [x, x**2 for x in vec] # error - parens required for tuples
File "<stdin>", line 1, in ?
[x, x**2 for x in vec]
^
SyntaxError: invalid syntax
>>> [(x, x**2) for x in vec]
[(2, 4), (4, 16), (6, 36)]
>>> vec1 = [2, 4, 6]
>>> vec2 = [4, 3, -9]
>>> [x*y for x in vec1 for y in vec2]
[8, 6, -18, 16, 12, -36, 24, 18, -54]
>>> [x+y for x in vec1 for y in vec2]
[6, 5, -7, 8, 7, -5, 10, 9, -3]
>>> [vec1[i]*vec2[i] for i in range(len(vec1))]
[8, 12, -54]
There is a way to remove an item from a list given its index instead of its value: the del statement. This can also be used to remove slices from a list (which we did earlier by assignment of an empty list to the slice). For example:
>>> a [-1, 1, 66.6, 333, 333, 1234.5] >>> del a[0] >>> a [1, 66.6, 333, 333, 1234.5] >>> del a[2:4] >>> a [1, 66.6, 1234.5]
del can also be used to delete entire variables:
>>> del a
Referencing the name a hereafter
is an error (at least until another value is assigned to it). We'll
find other uses for del later.
We saw that lists and strings have many common properties, such as indexing and slicing operations. They are two examples of sequence data types. Since Python is an evolving language, other sequence data types may be added. There is also another standard sequence data type: the tuple.
A tuple consists of a number of values separated by commas, for instance:
>>> t = 12345, 54321, 'hello!' >>> t[0] 12345 >>> t (12345, 54321, 'hello!') >>> # Tuples may be nested: ... u = t, (1, 2, 3, 4, 5) >>> u ((12345, 54321, 'hello!'), (1, 2, 3, 4, 5))
As you see, on output tuples are alway enclosed in parentheses, so that nested tuples are interpreted correctly; they may be input with or without surrounding parentheses, although often parentheses are necessary anyway (if the tuple is part of a larger expression).
Tuples have many uses. For example: (x, y) coordinate pairs, employee records from a database, etc. Tuples, like strings, are immutable: it is not possible to assign to the individual items of a tuple (you can simulate much of the same effect with slicing and concatenation, though). It is also possible to create tuples which contain mutable objects, such as lists.
A special problem is the construction of tuples containing 0 or 1 items: the syntax has some extra quirks to accommodate these. Empty tuples are constructed by an empty pair of parentheses; a tuple with one item is constructed by following a value with a comma (it is not sufficient to enclose a single value in parentheses). Ugly, but effective. For example:
>>> empty = ()
>>> singleton = 'hello', # <-- note trailing comma
>>> len(empty)
0
>>> len(singleton)
1
>>> singleton
('hello',)
The statement t = 12345, 54321,
'hello!' is an example of tuple packing: the values
12345, 54321 and 'hello!' are
packed together in a tuple. The reverse operation is also
possible:
>>> x, y, z = t
This is called, appropriately enough, sequence unpacking. Sequence unpacking requires that the list of variables on the left have the same number of elements as the length of the sequence. Note that multiple assignment is really just a combination of tuple packing and sequence unpacking!
There is a small bit of asymmetry here: packing multiple values always creates a tuple, and unpacking works for any sequence.
Another useful data type built into Python is the dictionary. Dictionaries are sometimes found in other languages as ``associative memories'' or ``associative arrays''. Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys, which can be any immutable type; strings and numbers can always be keys. Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key. You can't use lists as keys, since lists can be modified in place using their append() and extend() methods, as well as slice and indexed assignments.
It is best to think of a dictionary as an
unordered set of key: value pairs, with the requirement that
the keys are unique (within one dictionary). A pair of braces creates
an empty dictionary: {}. Placing a comma-separated list
of key:value pairs within the braces adds initial key:value pairs to
the dictionary; this is also the way dictionaries are written on
output.
The main operations on a dictionary are storing
a value with some key and extracting the value given the key. It is
also possible to delete a key:value pair with del. If
you store using a key that is already in use, the old value
associated with that key is forgotten. It is an error to extract a
value using a non-existent key.
The keys() method of a dictionary
object returns a list of all the keys used in the dictionary, in
random order (if you want it sorted, just apply the
sort() method to the list of keys). To check whether a
single key is in the dictionary, use the has_key()
method of the dictionary.
Here is a small example using a dictionary:
>>> tel = {'jack': 4098, 'sape': 4139}
>>> tel['guido'] = 4127
>>> tel
{'sape': 4139, 'guido': 4127, 'jack': 4098}
>>> tel['jack']
4098
>>> del tel['sape']
>>> tel['irv'] = 4127
>>> tel
{'guido': 4127, 'irv': 4127, 'jack': 4098}
>>> tel.keys()
['guido', 'irv', 'jack']
>>> tel.has_key('guido')
1
The conditions used in while and
if statements above can contain other operators besides
comparisons.
The comparison operators in and
not in check whether a value occurs (does not occur) in
a sequence. The operators is and is not
compare whether two objects are really the same object; this only
matters for mutable objects like lists. All comparison operators have
the same priority, which is lower than that of all numerical
operators.
Comparisons can be chained. For example,
a < b == c tests whether a is less than
b and moreover b equals
c.
Comparisons may be combined by the Boolean
operators and and or, and the outcome of a
comparison (or of any other Boolean expression) may be negated with
not. These all have lower priorities than comparison
operators again; between them, not has the highest
priority, and or the lowest, so that A and not B
or C is equivalent to (A and (not B)) or C. Of
course, parentheses can be used to express the desired
composition.
The Boolean operators and and
or are so-called shortcut operators: their
arguments are evaluated from left to right, and evaluation stops as
soon as the outcome is determined. E.g., if A and
C are true but B is false, A and B
and C does not evaluate the expression C. In general, the
return value of a shortcut operator, when used as a general value and
not as a Boolean, is the last evaluated argument.
It is possible to assign the result of a comparison or other Boolean expression to a variable. For example,
>>> string1, string2, string3 = '', 'Trondheim', 'Hammer Dance' >>> non_null = string1 or string2 or string3 >>> non_null 'Trondheim'
Note that in Python, unlike C, assignment
cannot occur inside expressions. C programmers may grumble about
this, but it avoids a common class of problems encountered in C
programs: typing = in an expression when ==
was intended.
Sequence objects may be compared to other objects with the same sequence type. The comparison uses lexicographical ordering: first the first two items are compared, and if they differ this determines the outcome of the comparison; if they are equal, the next two items are compared, and so on, until either sequence is exhausted. If two items to be compared are themselves sequences of the same type, the lexicographical comparison is carried out recursively. If all items of two sequences compare equal, the sequences are considered equal. If one sequence is an initial sub-sequence of the other, the shorter sequence is the smaller (lesser) one. Lexicographical ordering for strings uses the ASCII ordering for individual characters. Some examples of comparisons between sequences with the same types:
(1, 2, 3) < (1, 2, 4)
[1, 2, 3] < [1, 2, 4]
'ABC' < 'C' < 'Pascal' < 'Python'
(1, 2, 3, 4) < (1, 2, 4)
(1, 2) < (1, 2, -1)
(1, 2, 3) == (1.0, 2.0, 3.0)
(1, 2, ('aa', 'ab')) < (1, 2, ('abc', 'a'), 4)
Note that comparing objects of different types is legal. The outcome is deterministic but arbitrary: the types are ordered by their name. Thus, a list is always smaller than a string, a string is always smaller than a tuple, etc. Mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc
Choose one of the other chapters in the Scripting Guide:
Additional
documentation, product downloads and updates are at
www.PushToTest.com.
While the TestMaker software is distributed under
an open-source license, the documentation remains
(c) 2002 PushToTest. All rights
reserved.
