
x) 
a[len(a):] = [x]
.
L) 
a[len(a):] = L
.
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)
.
x) 
[i]) 
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. You will see this notation frequently in the
Python Library Reference.)
x) 
x) 
) 
) 
An example that uses most of the list methods:
>>> a = [66.25, 333, 333, 1, 1234.5] >>> print a.count(333), a.count(66.25), a.count('x') 2 1 0 >>> a.insert(2, 1) >>> a.append(333) >>> a [66.25, 333, 1, 333, 1, 1234.5, 333] >>> a.index(333) 1 >>> a.remove(333) >>> a [66.25, 1, 333, 1, 1234.5, 333] >>> a.reverse() >>> a [333, 1234.5, 1, 333, 1, 66.25] >>> a.sort() >>> a [1, 1, 66.25, 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 (``lastin, firstout''). 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 (``firstin,
firstout''). 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 builtin 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:
>>> 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). For example:
>>> seq = range(8) >>> def add(x, y): return x+y ... >>> map(add, seq, seq) [0, 2, 4, 6, 8, 10, 12, 14]
"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:
>>> 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
Don't use this example's definition of sum(): since summing
numbers is such a common need, a builtin 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']
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.
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:
>>> 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 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.
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'])
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 commaseparated 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 nonexistent 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 keyvalue pairs stored as tuples. When the pairs form a pattern, list comprehensions can compactly specify the keyvalue 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 keyvalues pairs to the dict() constructor.
When the keys are simple strings, it is sometimes easier to specify pairs using keyword arguments:
>>> dict(sape=4139, guido=4127, jack=4098) {'sape': 4139, 'jack': 4098, 'guido': 4127}
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
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 socalled
shortcircuit 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 shortcircuit 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.
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 subsequence 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:
(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. ^{5.1} Mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc.