5. Data Structures¶
This chapter describes some things you’ve learned about already in more detail, and adds some new things as well.
5.1. More on Lists¶
The list data type has some more methods. Here are all of the methods of list objects:

list.
append
(x) Add an item to the end of the list; equivalent to
a[len(a):] = [x]
.

list.
extend
(L) Extend the list by appending all the items in the given list; equivalent to
a[len(a):] = L
.

list.
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, anda.insert(len(a), x)
is equivalent toa.append(x)
.

list.
remove
(x) Remove the first item from the list whose value is x. It is an error if there is no such item.

list.
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. You will see this notation frequently in the Python Library Reference.)

list.
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.

list.
count
(x) Return the number of times x appears in the list.

list.
sort
(cmp=None, key=None, reverse=False) Sort the items of the list in place (the arguments can be used for sort customization, see
sorted()
for their explanation).

list.
reverse
() Reverse the elements of the list, in place.
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]
>>> a.pop()
1234.5
>>> a
[1, 1, 66.25, 333, 333]
You might have noticed that methods like insert
, remove
or sort
that
only modify the list have no return value printed – they return the default
None
. This is a design principle for all mutable data structures in
Python.
5.1.1. Using Lists as Stacks¶
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]
5.1.2. Using Lists as Queues¶
It is also possible to use a list as a queue, where the first element added is the first element retrieved (“firstin, firstout”); however, lists are not efficient for this purpose. While appends and pops from the end of list are fast, doing inserts or pops from the beginning of a list is slow (because all of the other elements have to be shifted by one).
To implement a queue, use collections.deque
which was designed to
have fast appends and pops from both ends. For example:
>>> from collections import deque
>>> queue = deque(["Eric", "John", "Michael"])
>>> queue.append("Terry") # Terry arrives
>>> queue.append("Graham") # Graham arrives
>>> queue.popleft() # The first to arrive now leaves
'Eric'
>>> queue.popleft() # The second to arrive now leaves
'John'
>>> queue # Remaining queue in order of arrival
deque(['Michael', 'Terry', 'Graham'])
5.1.3. Functional Programming Tools¶
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
str
, unicode
or tuple
, the result will be of the
same type; otherwise, it is always a list
. For example, to compute a
sequence of numbers divisible by 3 or 5:
>>> def f(x): return x % 3 == 0 or x % 5 == 0
...
>>> filter(f, range(2, 25))
[3, 5, 6, 9, 10, 12, 15, 18, 20, 21, 24]
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.
5.1.4. List Comprehensions¶
List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable, or to create a subsequence of those elements that satisfy a certain condition.
For example, assume we want to create a list of squares, like:
>>> squares = []
>>> for x in range(10):
... squares.append(x**2)
...
>>> squares
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
We can obtain the same result with:
squares = [x**2 for x in range(10)]
This is also equivalent to squares = map(lambda x: x**2, range(10))
,
but it’s more concise and readable.
A list comprehension consists of brackets containing an expression followed
by a for
clause, then zero or more for
or if
clauses. The result will be a new list resulting from evaluating the expression
in the context of the for
and if
clauses which follow it.
For example, this listcomp combines the elements of two lists if they are not
equal:
>>> [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]
[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]
and it’s equivalent to:
>>> combs = []
>>> for x in [1,2,3]:
... for y in [3,1,4]:
... if x != y:
... combs.append((x, y))
...
>>> combs
[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]
Note how the order of the for
and if
statements is the
same in both these snippets.
If the expression is a tuple (e.g. the (x, y)
in the previous example),
it must be parenthesized.
>>> vec = [4, 2, 0, 2, 4]
>>> # create a new list with the values doubled
>>> [x*2 for x in vec]
[8, 4, 0, 4, 8]
>>> # filter the list to exclude negative numbers
>>> [x for x in vec if x >= 0]
[0, 2, 4]
>>> # apply a function to all the elements
>>> [abs(x) for x in vec]
[4, 2, 0, 2, 4]
>>> # call a method on each element
>>> freshfruit = [' banana', ' loganberry ', 'passion fruit ']
>>> [weapon.strip() for weapon in freshfruit]
['banana', 'loganberry', 'passion fruit']
>>> # create a list of 2tuples like (number, square)
>>> [(x, x**2) for x in range(6)]
[(0, 0), (1, 1), (2, 4), (3, 9), (4, 16), (5, 25)]
>>> # the tuple must be parenthesized, otherwise an error is raised
>>> [x, x**2 for x in range(6)]
File "<stdin>", line 1, in <module>
[x, x**2 for x in range(6)]
^
SyntaxError: invalid syntax
>>> # flatten a list using a listcomp with two 'for'
>>> vec = [[1,2,3], [4,5,6], [7,8,9]]
>>> [num for elem in vec for num in elem]
[1, 2, 3, 4, 5, 6, 7, 8, 9]
List comprehensions can contain complex expressions and nested functions:
>>> from math import pi
>>> [str(round(pi, i)) for i in range(1, 6)]
['3.1', '3.14', '3.142', '3.1416', '3.14159']
5.1.4.1. Nested List Comprehensions¶
The initial expression in a list comprehension can be any arbitrary expression, including another list comprehension.
Consider the following example of a 3x4 matrix implemented as a list of 3 lists of length 4:
>>> matrix = [
... [1, 2, 3, 4],
... [5, 6, 7, 8],
... [9, 10, 11, 12],
... ]
The following list comprehension will transpose rows and columns:
>>> [[row[i] for row in matrix] for i in range(4)]
[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]
As we saw in the previous section, the nested listcomp is evaluated in
the context of the for
that follows it, so this example is
equivalent to:
>>> transposed = []
>>> for i in range(4):
... transposed.append([row[i] for row in matrix])
...
>>> transposed
[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]
which, in turn, is the same as:
>>> transposed = []
>>> for i in range(4):
... # the following 3 lines implement the nested listcomp
... transposed_row = []
... for row in matrix:
... transposed_row.append(row[i])
... transposed.append(transposed_row)
...
>>> transposed
[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]
In the real world, you should prefer builtin functions to complex flow statements.
The zip()
function would do a great job for this use case:
>>> zip(*matrix)
[(1, 5, 9), (2, 6, 10), (3, 7, 11), (4, 8, 12)]
See Unpacking Argument Lists for details on the asterisk in this line.
5.2. 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.
5.3. 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 (see Sequence Types — str, unicode, list, tuple, bytearray, buffer, xrange). 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))
>>> # Tuples are immutable:
... t[0] = 88888
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'tuple' object does not support item assignment
>>> # but they can contain mutable objects:
... v = ([1, 2, 3], [3, 2, 1])
>>> v
([1, 2, 3], [3, 2, 1])
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). It is not possible to assign to the individual items of a tuple, however it is possible to create tuples which contain mutable objects, such as lists.
Though tuples may seem similar to lists, they are often used in different
situations and for different purposes.
Tuples are immutable, and usually contain a heterogeneous sequence of
elements that are accessed via unpacking (see later in this section) or indexing
(or even by attribute in the case of namedtuples
).
Lists are mutable, and their elements are usually homogeneous and are
accessed by iterating over the list.
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 and works for any sequence on the righthand side. 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.
5.4. 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.
Curly braces or the set()
function can be used to create sets. Note: to
create an empty set you have to use set()
, not {}
; the latter creates an
empty dictionary, a data structure that we discuss in the next section.
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'])
Similarly to list comprehensions, set comprehensions are also supported:
>>> a = {x for x in 'abracadabra' if x not in 'abc'}
>>> a
set(['r', 'd'])
5.5. Dictionaries¶
Another useful data type built into Python is the dictionary (see
Mapping Types — dict). 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 sorted()
function to it). To check whether a single key is in the
dictionary, use 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']
>>> 'guido' in tel
True
The dict()
constructor builds dictionaries directly from sequences of
keyvalue pairs:
>>> dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])
{'sape': 4139, 'jack': 4098, 'guido': 4127}
In addition, dict comprehensions can be used to create dictionaries from arbitrary key and value expressions:
>>> {x: x**2 for x in (2, 4, 6)}
{2: 4, 4: 16, 6: 36}
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}
5.6. Looping Techniques¶
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 {0}? It is {1}.'.format(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
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
It is sometimes tempting to change a list while you are looping over it; however, it is often simpler and safer to create a new list instead.
>>> import math
>>> raw_data = [56.2, float('NaN'), 51.7, 55.3, 52.5, float('NaN'), 47.8]
>>> filtered_data = []
>>> for value in raw_data:
... if not math.isnan(value):
... filtered_data.append(value)
...
>>> filtered_data
[56.2, 51.7, 55.3, 52.5, 47.8]
5.7. 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 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.
5.8. 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 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. 1 Mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc.
Footnotes
 1
The rules for comparing objects of different types should not be relied upon; they may change in a future version of the language.