This chapter describes some things you've learned about already in more
detail, and adds some new things as well.
A D V E R T I S E M E N T
More on Lists
The list data type has some more methods. Here are all of the methods of list
objects:
append(
x)
Add an item to the end of the list; equivalent to a[len(a):] = [x].
extend(
L)
Extend the list by appending all the items in the given list; equivalent
to a[len(a):] = L.
insert(
i, x)
Insert an item at a given position. The first argument is the index of
the element before which to insert, so 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)
Remove the first item from the list whose value is x. It is
an error if there is no such item.
pop(
[i])
Remove the item at the given position in the list, and return it. If no
index is specified, a.pop() removes and returns the last item
in the list. (The square brackets around the i in the method
signature denote that the parameter is optional, not that you should type
square brackets at that position.)
index(
x)
Return the index in the list of the first item whose value is x.
It is an error if there is no such item.
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:
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:
There are three built-in functions that are very useful when used with lists:
filter(), map(), and
reduce().
"filter(function, sequence)"
returns a sequence consisting of those items from the sequence for which function(item) is true. If sequence is a
string or tuple, the result will
be of the same type; otherwise, it is always a list. 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:
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).
For example:
"reduce(function, sequence)"
returns a single value constructed by calling the binary function function
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:
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,
Don't use this example's definition of sum(): since
summing numbers is such a common need, a built-in function sum(sequence)
is already provided, and works exactly like this. New
in version 2.3.
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 followed 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] # 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]
List comprehensions are much more flexible than map()
and can be applied to complex expressions and nested functions:
>>> [str(round(355/113.0, i)) for i in range(1,6)]
['3.1', '3.14', '3.142', '3.1416', '3.14159']
The del statement
There is a way to remove an item from a list given its index instead of its
value: the del statement. This differs from the
pop() method which returns a value. The
del statement can also be used to remove slices from a
list or clear the entire list (which we did earlier by assignment of an empty
list to the slice). For example:
>>> a = [-1, 1, 66.25, 333, 333, 1234.5]
>>> del a[0]
>>> a
[1, 66.25, 333, 333, 1234.5]
>>> del a[2:4]
>>> a
[1, 66.25, 1234.5]
>>> del a[:]
>>> a
[]
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.
Tuples and Sequences
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 always 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:
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 the list of variables on the left to 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.
Sets
Python also includes a data type for sets. A set is an unordered
collection with no duplicate elements. Basic uses include membership testing and
eliminating duplicate entries. Set objects also support mathematical operations
like union, intersection, difference, and symmetric difference.
Here is a brief demonstration:
>>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
>>> fruit = set(basket) # create a set without duplicates
>>> fruit
set(['orange', 'pear', 'apple', 'banana'])
>>> 'orange' in fruit # fast membership testing
True
>>> 'crabgrass' in fruit
False
>>> # Demonstrate set operations on unique letters from two words
...
>>> a = set('abracadabra')
>>> b = set('alacazam')
>>> a # unique letters in a
set(['a', 'r', 'b', 'c', 'd'])
>>> a - b # letters in a but not in b
set(['r', 'd', 'b'])
>>> a | b # letters in either a or b
set(['a', 'c', 'r', 'd', 'b', 'm', 'z', 'l'])
>>> a & b # letters in both a and b
set(['a', 'c'])
>>> a ^ b # letters in a or b but not both
set(['r', 'd', 'b', 'm', 'z', 'l'])
Dictionaries
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
index assignments, slice assignments, or methods like
append() and extend().
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 arbitrary 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, either use the
dictionary's has_key() method or the
in keyword.
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')
True
>>> 'guido' in tel
True
The dict() constructor builds dictionaries directly
from lists of key-value pairs stored as tuples. When the pairs form a pattern,
list comprehensions can compactly specify the key-value list.
>>> dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])
{'sape': 4139, 'jack': 4098, 'guido': 4127}
>>> dict([(x, x**2) for x in (2, 4, 6)]) # use a list comprehension
{2: 4, 4: 16, 6: 36}
Later in the tutorial, we will learn about Generator Expressions which are
even better suited for the task of supplying key-values pairs to the
dict() constructor.
When the keys are simple strings, it is sometimes easier to specify pairs
using keyword arguments:
When looping through dictionaries, the key and corresponding value can be
retrieved at the same time using the iteritems() method.
>>> knights = {'gallahad': 'the pure', 'robin': 'the brave'}
>>> for k, v in knights.iteritems():
... print k, v
...
gallahad the pure
robin the brave
When looping through a sequence, the position index and corresponding value
can be retrieved at the same time using the enumerate()
function.
>>> for i, v in enumerate(['tic', 'tac', 'toe']):
... print i, v
...
0 tic
1 tac
2 toe
To loop over two or more sequences at the same time, the entries can be
paired with the zip() function.
>>> questions = ['name', 'quest', 'favorite color']
>>> answers = ['lancelot', 'the holy grail', 'blue']
>>> for q, a in zip(questions, answers):
... print 'What is your %s? It is %s.' % (q, a)
...
What is your name? It is lancelot.
What is your quest? It is the holy grail.
What is your favorite color? It is blue.
To loop over a sequence in reverse, first specify the sequence in a forward
direction and then call the reversed() function.
>>> for i in reversed(xrange(1,10,2)):
... print i
...
9
7
5
3
1
To loop over a sequence in sorted order, use the
sorted() function which returns a new sorted list while leaving the source
unaltered.
>>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
>>> for f in sorted(set(basket)):
... print f
...
apple
banana
orange
pear
More on Conditions
The conditions used in while and if statements can
contain any operators, not just 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 using the Boolean operators and and
or, and the outcome of a comparison (or of any other Boolean
expression) may be negated with not. These have lower priorities
than comparison operators; 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. As always, parentheses can
be used to express the desired composition.
The Boolean operators and and or are so-called
short-circuit operators: their arguments are evaluated from left to right,
and evaluation stops as soon as the outcome is determined. For example, if
A and C are true but B is false, A and B
and C does not evaluate the expression C. When used as a
general value and not as a Boolean, the return value of a short-circuit operator
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.
Comparing Sequences and Other Types
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 of the same type:
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.
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