Like a string, a list is a sequence of values. In a string, the values are characters; in a list, they can be any type. The values in a list are called elements or sometimes items.
There are several ways to create a new list; the simplest is to
enclose the elements in square brackets ([
and ]
):
[
10
,
20
,
30
,
40
]
[
'crunchy frog'
,
'ram bladder'
,
'lark vomit'
]
The first example is a list of four integers. The second is a list of three strings. The elements of a list don’t have to be the same type. The following list contains a string, a float, an integer, and (lo!) another list:
[
'spam'
,
2.0
,
5
,
[
10
,
20
]]
A list within another list is nested.
A list that contains no elements is called an empty list; you can
create one with empty brackets, []
.
As you might expect, you can assign list values to variables:
>>> cheeses = ['Cheddar', 'Edam', 'Gouda'] >>> numbers = [17, 123] >>> empty = [] >>> print cheeses, numbers, empty ['Cheddar', 'Edam', 'Gouda'] [17, 123] []
The syntax for accessing the elements of a list is the same as for accessing the characters of a string—the bracket operator. The expression inside the brackets specifies the index. Remember that the indices start at 0:
>>> print cheeses[0] Cheddar
Unlike strings, lists are mutable. When the bracket operator appears on the left side of an assignment, it identifies the element of the list that will be assigned.
>>> numbers = [17, 123] >>> numbers[1] = 5 >>> print numbers [17, 5]
The one-eth element of numbers
,
which used to be 123, is now 5.
You can think of a list as a relationship between indices and
elements. This relationship is called a mapping; each index “maps to” one of the
elements. Figure 10-1 shows the state diagram for
cheeses
, numbers
and empty
:
Lists are represented by boxes with the word “list” outside and
the elements of the list inside. cheeses
refers to a list with three elements
indexed 0, 1 and 2. numbers
contains
two elements; the diagram shows that the value of the second element has
been reassigned from 123 to 5. empty
refers to a list with no elements.
List indices work the same way as string indices:
The in
operator also
works on lists.
>>> cheeses = ['Cheddar', 'Edam', 'Gouda'] >>> 'Edam' in cheeses True >>> 'Brie' in cheeses False
The most common way to traverse the elements of a list is
with a for
loop. The syntax is the
same as for strings:
for
cheese
in
cheeses
:
cheese
This works well if you only need to read the elements of the list.
But if you want to write or update the elements, you need the indices. A
common way to do that is to combine the functions range
and len
:
for
i
in
range
(
len
(
numbers
)):
numbers
[
i
]
=
numbers
[
i
]
*
2
This loop traverses the list and updates each element. len
returns the number of elements in the
list. range
returns a list of indices
from 0 to n-1, where n is the
length of the list. Each time through the loop i
gets the index of the next element. The
assignment statement in the body uses i
to read the old value of the element and to
assign the new value.
A for
loop over an empty list
never executes the body:
for
x
in
[]:
'This never happens.'
Although a list can contain another list, the nested list still counts as a single element. The length of this list is four:
[
'spam'
,
1
,
[
'Brie'
,
'Roquefort'
,
'Pol le Veq'
],
[
1
,
2
,
3
]]
The +
operator
concatenates lists:
>>> a = [1, 2, 3] >>> b = [4, 5, 6] >>> c = a + b >>> print c [1, 2, 3, 4, 5, 6]
Similarly, the *
operator
repeats a list a given number of times:
>>> [0] * 4 [0, 0, 0, 0] >>> [1, 2, 3] * 3 [1, 2, 3, 1, 2, 3, 1, 2, 3]
The first example repeats [0]
four times. The second example repeats the list [1, 2, 3]
three times.
The slice operator also works on lists:
>>> t = ['a', 'b', 'c', 'd', 'e', 'f'] >>> t[1:3] ['b', 'c'] >>> t[:4] ['a', 'b', 'c', 'd'] >>> t[3:] ['d', 'e', 'f']
If you omit the first index, the slice starts at the beginning. If you omit the second, the slice goes to the end. So if you omit both, the slice is a copy of the whole list.
>>> t[:] ['a', 'b', 'c', 'd', 'e', 'f']
Since lists are mutable, it is often useful to make a copy before performing operations that fold, spindle or mutilate lists.
A slice operator on the left side of an assignment can update multiple elements:
>>> t = ['a', 'b', 'c', 'd', 'e', 'f'] >>> t[1:3] = ['x', 'y'] >>> print t ['a', 'x', 'y', 'd', 'e', 'f']
Python provides methods that operate on lists. For
example, append
adds a new element to
the end of a list:
>>> t = ['a', 'b', 'c'] >>> t.append('d') >>> print t ['a', 'b', 'c', 'd']
extend
takes a list as an
argument and appends all of the elements:
>>> t1 = ['a', 'b', 'c'] >>> t2 = ['d', 'e'] >>> t1.extend(t2) >>> print t1 ['a', 'b', 'c', 'd', 'e']
This example leaves t2
unmodified.
sort
arranges the elements of
the list from low to high:
>>> t = ['d', 'c', 'e', 'b', 'a'] >>> t.sort() >>> print t ['a', 'b', 'c', 'd', 'e']
List methods are all void; they modify the list and return
None
. If you accidentally write
t = t.sort()
, you will be
disappointed with the result.
To add up all the numbers in a list, you can use a loop like this:
def
add_all
(
t
):
total
=
0
for
x
in
t
:
total
+=
x
return
total
total
is initialized to 0. Each
time through the loop, x
gets one
element from the list. The +=
operator provides a short way to update a variable. This augmented assignment statement:
total
+=
x
is equivalent to:
total
=
total
+
x
As the loop executes, total
accumulates the sum of the elements; a variable used this way is
sometimes called an accumulator.
Adding up the elements of a list is such a common operation that
Python provides it as a built-in function, sum
:
>>> t = [1, 2, 3] >>> sum(t) 6
An operation like this that combines a sequence of elements into a single value is sometimes called reduce.
Exercise 10-1.
Write a function called nested_sum
that takes a nested list of integers
and add up the elements from all of the nested lists.
Sometimes you want to traverse one list while building another. For example, the following function takes a list of strings and returns a new list that contains capitalized strings:
def
capitalize_all
(
t
):
res
=
[]
for
s
in
t
:
res
.
append
(
s
.
capitalize
())
return
res
res
is initialized with an
empty list; each time through the loop, we append the next element. So
res
is another kind of
accumulator.
An operation like capitalize_all
is sometimes called a map because it “maps” a function (in this case
the method capitalize
) onto each of
the elements in a sequence.
Exercise 10-2.
Use capitalize_all
to write a function named
capitalize_nested
that
takes a nested list of strings and returns a new nested list with all
strings capitalized.
Another common operation is to select some of the elements from a list and return a sublist. For example, the following function takes a list of strings and returns a list that contains only the uppercase strings:
def
only_upper
(
t
):
res
=
[]
for
s
in
t
:
if
s
.
isupper
():
res
.
append
(
s
)
return
res
isupper
is a string method that
returns True
if the string contains
only upper case letters.
An operation like only_upper
is called a filter because it selects some of the elements
and filters out the others.
Most common list operations can be expressed as a combination of
map, filter and reduce. Because these operations are so common, Python
provides language features to support them, including the built-in
function map
and an operator called a
“list comprehension.”
There are several ways to delete elements from a list. If
you know the index of the element you want, you can use pop
:
>>> t = ['a', 'b', 'c'] >>> x = t.pop(1) >>> print t ['a', 'c'] >>> print x b
pop
modifies the list and
returns the element that was removed. If you don’t provide an index, it
deletes and returns the last element.
If you don’t need the removed value, you can use the del
operator:
>>> t = ['a', 'b', 'c'] >>> del t[1] >>> print t ['a', 'c']
If you know the element you want to remove (but not the index),
you can use remove
:
>>> t = ['a', 'b', 'c'] >>> t.remove('b') >>> print t ['a', 'c']
The return value from remove
is
None
.
To remove more than one element, you can use del
with a slice index:
>>> t = ['a', 'b', 'c', 'd', 'e', 'f'] >>> del t[1:5] >>> print t ['a', 'f']
As usual, the slice selects all the elements up to, but not including, the second index.
A string is a sequence of characters and a list is a
sequence of values, but a list of characters is not the same as a
string. To convert from a string to a list of characters, you can use
list
:
>>> s = 'spam' >>> t = list(s) >>> print t ['s', 'p', 'a', 'm']
Because list
is the name of a
built-in function, you should avoid using it as a variable name. I also
avoid l
because it looks too much
like 1
. So that’s why I use t
.
The list
function breaks a
string into individual letters. If you want to break a string into
words, you can use the split
method:
>>> s = 'pining for the fjords' >>> t = s.split() >>> print t ['pining', 'for', 'the', 'fjords']
An optional argument called a delimiter specifies which characters to use as word boundaries. The following example uses a hyphen as a delimiter:
>>> s = 'spam-spam-spam' >>> delimiter = '-' >>> s.split(delimiter) ['spam', 'spam', 'spam']
join
is the inverse of split
. It takes a list of strings and
concatenates the elements. join
is a
string method, so you have to invoke it on the delimiter and pass the
list as a parameter:
>>> t = ['pining', 'for', 'the', 'fjords'] >>> delimiter = ' ' >>> delimiter.join(t) 'pining for the fjords'
In this case the delimiter is a space character, so join
puts a space between words. To
concatenate strings without spaces, you can use the empty string,
''
, as a
delimiter.
If we execute these assignment statements:
a
=
'banana'
b
=
'banana'
We know that a
and b
both refer to a string, but we don’t know
whether they refer to the same string. There are
two possible states, shown in Figure 10-2.
In one case, a
and b
refer to two different objects that have the
same value. In the second case, they refer to the same object.
To check whether two variables refer to the same object, you can
use the is
operator.
>>> a = 'banana' >>> b = 'banana' >>> a is b True
In this example, Python only created one string object, and both
a
and b
refer to it.
But when you create two lists, you get two objects:
>>> a = [1, 2, 3] >>> b = [1, 2, 3] >>> a is b False
So the state diagram looks like Figure 10-3.
In this case we would say that the two lists are equivalent, because they have the same elements, but not identical, because they are not the same object. If two objects are identical, they are also equivalent, but if they are equivalent, they are not necessarily identical.
Until now, we have been using “object” and “value”
interchangeably, but it is more precise to say that an object has a
value. If you execute [1,2,3]
, you
get a list object whose value is a sequence of integers. If another list
has the same elements, we say it has the same value, but it is not the
same object.
If a
refers to an
object and you assign b = a
, then
both variables refer to the same object:
>>> a = [1, 2, 3] >>> b = a >>> b is a True
The state diagram looks like Figure 10-4.
The association of a variable with an object is called a reference. In this example, there are two references to the same object.
An object with more than one reference has more than one name, so we say that the object is aliased.
If the aliased object is mutable, changes made with one alias affect the other:
>>> b[0] = 17 >>> print a [17, 2, 3]
Although this behavior can be useful, it is error-prone. In general, it is safer to avoid aliasing when you are working with mutable objects.
For immutable objects like strings, aliasing is not as much of a problem. In this example:
a
=
'banana'
b
=
'banana'
It almost never makes a difference whether a
and b
refer to the same string or not.
When you pass a list to a function, the function gets a
reference to the list. If the function modifies a list parameter, the
caller sees the change. For example, delete_head
removes the first element from a
list:
def
delete_head
(
t
):
del
t
[
0
]
Here’s how it is used:
>>> letters = ['a', 'b', 'c'] >>> delete_head(letters) >>> print letters ['b', 'c']
The parameter t
and the
variable letters
are aliases for the
same object. The stack diagram looks like Figure 10-5.
Since the list is shared by two frames, I drew it between them.
It is important to distinguish between operations that modify
lists and operations that create new lists. For example, the append
method modifies a list, but the
+
operator creates a new
list:
>>> t1 = [1, 2] >>> t2 = t1.append(3) >>> print t1 [1, 2, 3] >>> print t2 None >>> t3 = t1 + [4] >>> print t3 [1, 2, 3, 4]
This difference is important when you write functions that are supposed to modify lists. For example, this function does not delete the head of a list:
def
bad_delete_head
(
t
):
t
=
t
[
1
:]
# WRONG!
The slice operator creates a new list and the assignment makes
t
refer to it, but none of that has
any effect on the list that was passed as an argument.
An alternative is to write a function that creates and returns a
new list. For example, tail
returns
all but the first element of a list:
def
tail
(
t
):
return
t
[
1
:]
This function leaves the original list unmodified. Here’s how it is used:
>>> letters = ['a', 'b', 'c'] >>> rest = tail(letters) >>> print rest ['b', 'c']
Careless use of lists (and other mutable objects) can lead to long hours of debugging. Here are some common pitfalls and ways to avoid them:
Don’t forget that most list methods modify the argument and
return None
. This is the opposite
of the string methods, which return a new string and leave the
original alone.
If you are used to writing string code like this:
word
=
word
.
strip
()
It is tempting to write list code like this:
t
=
t
.
sort
()
# WRONG!
Because sort
returns None
, the next operation
you perform with t
is likely to
fail.
Before using list methods and operators, you should read the documentation carefully and then test them in interactive mode. The methods and operators that lists share with other sequences (like strings) are documented at http://docs.python.org/lib/typesseq.html. The methods and operators that only apply to mutable sequences are documented at http://docs.python.org/lib/typesseq-mutable.html.
Pick an idiom and stick with it.
Part of the problem with lists is that there are too many ways
to do things. For example, to remove an element from a list, you can
use pop
, remove
, del
, or even a slice assignment.
To add an element, you can use the append
method or the +
operator. Assuming that t
is a list and x
is a list element, these are
right:
t
.
append
(
x
)
t
=
t
+
[
x
]
And these are wrong:
t
.
append
([
x
])
# WRONG!
t
=
t
.
append
(
x
)
# WRONG!
t
+
[
x
]
# WRONG!
t
=
t
+
x
# WRONG!
Try out each of these examples in interactive mode to make sure you understand what they do. Notice that only the last one causes a runtime error; the other three are legal, but they do the wrong thing.
Make copies to avoid aliasing.
If you want to use a method like sort
that modifies the argument, but you
need to keep the original list as well, you can make a copy.
orig
=
t
[:]
t
.
sort
()
In this example you could also use the built-in function
sorted
, which returns a new,
sorted list and leaves the original alone. But in that case you
should avoid using sorted
as a
variable name!
One of the values in a list (or other sequence), also called items.
A relationship in which each element of one set corresponds to an element of another set. For example, a list is a mapping from indices to elements.
A statement that updates the value of a variable using an
operator like +=
.
A processing pattern that traverses a sequence and accumulates the elements into a single result.
A processing pattern that traverses a sequence and performs an operation on each element.
A processing pattern that traverses a list and selects the elements that satisfy some criterion.
Something a variable can refer to. An object has a type and a value.
A circumstance where two or more variables refer to the same object.
A character or string used to indicate where a string should be split.
Exercise 10-6.
Write a function called is_sorted
that takes a list as a parameter and
returns True
if the list is sorted
in ascending order and False
otherwise. You can assume (as a precondition) that the elements of the
list can be compared with the relational operators <
, >
, etc.
For example, is_sorted([1,2,2])
should return True
and is_sorted(['b','a'])
should return False
.
Exercise 10-8.
The (so-called) Birthday Paradox:
Write a function called has_duplicates
that takes a list and
returns True
if there is any
element that appears more than once. It should not modify the
original list.
If there are 23 students in your class, what are the chances
that two of you have the same birthday? You can estimate this
probability by generating random samples of 23 birthdays and
checking for matches. Hint: you can generate random birthdays with
the randint
function in the
random
module.
You can read about this problem at http://en.wikipedia.org/wiki/Birthday_paradox, and you can download my solution from http://thinkpython.com/code/birthday.py.
Exercise 10-10.
Write a function that reads the file words.txt
and builds a list with one element
per word. Write two versions of this function, one using the append
method and the other using the idiom
t = t + [x]
. Which one takes longer
to run? Why?
Hint: use the time
module to
measure elapsed time. Solution: http://thinkpython.com/code/wordlist.py.
Exercise 10-11.
To check whether a word is in the word list, you could
use the in
operator, but it would
be slow because it searches through the words in order.
Because the words are in alphabetical order, we can speed things up with a bisection search (also known as binary search), which is similar to what you do when you look a word up in the dictionary. You start in the middle and check to see whether the word you are looking for comes before the word in the middle of the list. If so, then you search the first half of the list the same way. Otherwise you search the second half.
Either way, you cut the remaining search space in half. If the word list has 113,809 words, it will take about 17 steps to find the word or conclude that it’s not there.
Write a function called bisect
that takes a sorted list and a target
value and returns the index of the value in the list, if it’s there,
or None
if it’s not.
Or you could read the documentation of the bisect
module and use that! Solution: http://thinkpython.com/code/inlist.py.
Exercise 10-12.
Two words are a “reverse pair” if each is the reverse of the other. Write a program that finds all the reverse pairs in the word list. Solution: http://thinkpython.com/code/reverse_pair.py.
Exercise 10-13.
Two words “interlock” if taking alternating letters from each forms a new word. For example, “shoe” and “cold” interlock to form “schooled.” Solution: http://thinkpython.com/code/interlock.py. Credit: This exercise is inspired by an example at http://puzzlers.org.
Write a program that finds all pairs of words that interlock. Hint: don’t enumerate all pairs!
Can you find any words that are three-way interlocked; that is, every third letter forms a word, starting from the first, second or third?
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