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List Assignment Python

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:


Add an item to the end of the list; equivalent to .


Extend the list by appending all the items in the given list; equivalent to .

(i, x)

Insert an item at a given position. The first argument is the index of the element before which to insert, so inserts at the front of the list, and is equivalent to .


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


Remove the item at the given position in the list, and return it. If no index is specified, 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.)


Return the index in the list of the first item whose value is x. It is an error if there is no such item.


Return the number of times x appears in the list.

(cmp=None, key=None, reverse=False)

Sort the items of the list in place (the arguments can be used for sort customization, see for their explanation).


Reverse the elements of the list, in place.

An example that uses most of the list methods:

You might have noticed that methods like , or that only modify the list have no return value printed – they return the default . 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 (“last-in, first-out”). To add an item to the top of the stack, use . To retrieve an item from the top of the stack, use 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 (“first-in, first-out”); 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 which was designed to have fast appends and pops from both ends. For example:

>>> fromcollectionsimportdeque>>> 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 arrivaldeque(['Michael', 'Terry', 'Graham'])

5.1.3. Functional Programming Tools¶

There are three built-in functions that are very useful when used with lists: , , and .

returns a sequence consisting of those items from the sequence for which is true. If sequence is a , or , the result will be of the same type; otherwise, it is always a . For example, to compute a sequence of numbers divisible by 3 or 5:

calls 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 if some sequence is shorter than another). For example:

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 : since summing numbers is such a common need, a built-in function is already provided, and works exactly like this.

>>> deff(x):returnx%3==0orx%5==0...>>> filter(f,range(2,25))[3, 5, 6, 9, 10, 12, 15, 18, 20, 21, 24]
>>> defcube(x):returnx*x*x...>>> map(cube,range(1,11))[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]
>>> seq=range(8)>>> defadd(x,y):returnx+y...>>> map(add,seq,seq)[0, 2, 4, 6, 8, 10, 12, 14]
>>> defadd(x,y):returnx+y...>>> reduce(add,range(1,11))55
>>> defsum(seq):... defadd(x,y):returnx+y... returnreduce(add,seq,0)...>>> sum(range(1,11))55>>> sum([])0

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:

We can obtain the same result with:

This is also equivalent to , but it’s more concise and readable.

A list comprehension consists of brackets containing an expression followed by a clause, then zero or more or clauses. The result will be a new list resulting from evaluating the expression in the context of the and clauses which follow it. For example, this listcomp combines the elements of two lists if they are not equal:

and it’s equivalent to:

Note how the order of the and statements is the same in both these snippets.

If the expression is a tuple (e.g. the in the previous example), it must be parenthesized.

List comprehensions can contain complex expressions and nested functions: 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:

The following list comprehension will transpose rows and columns:

As we saw in the previous section, the nested listcomp is evaluated in the context of the that follows it, so this example is equivalent to:

which, in turn, is the same as:

In the real world, you should prefer built-in functions to complex flow statements. The function would do a great job for this use case:

See Unpacking Argument Lists for details on the asterisk in this line.

>>> matrix=[... [1,2,3,4],... [5,6,7,8],... [9,10,11,12],... ]
>>> [[row[i]forrowinmatrix]foriinrange(4)][[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]
>>> transposed=[]>>> foriinrange(4):... transposed.append([row[i]forrowinmatrix])...>>> transposed[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]
>>> transposed=[]>>> foriinrange(4):... # the following 3 lines implement the nested listcomp... transposed_row=[]... forrowinmatrix:... transposed_row.append(row[i])... transposed.append(transposed_row)...>>> transposed[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]
>>> zip(*matrix)[(1, 5, 9), (2, 6, 10), (3, 7, 11), (4, 8, 12)]
>>> squares=[]>>> forxinrange(10):... squares.append(x**2)...>>> squares[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
>>> [(x,y)forxin[1,2,3]foryin[3,1,4]ifx!=y][(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]
>>> combs=[]>>> forxin[1,2,3]:... foryin[3,1,4]:... ifx!=y:... combs.append((x,y))...>>> combs[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]
>>> vec=[-4,-2,0,2,4]>>> # create a new list with the values doubled>>> [x*2forxinvec][-8, -4, 0, 4, 8]>>> # filter the list to exclude negative numbers>>> [xforxinvecifx>=0][0, 2, 4]>>> # apply a function to all the elements>>> [abs(x)forxinvec][4, 2, 0, 2, 4]>>> # call a method on each element>>> freshfruit=[' banana',' loganberry ','passion fruit ']>>> [weapon.strip()forweaponinfreshfruit]['banana', 'loganberry', 'passion fruit']>>> # create a list of 2-tuples like (number, square)>>> [(x,x**2)forxinrange(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**2forxinrange(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]]>>> [numforeleminvecfornuminelem][1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> frommathimportpi>>> [str(round(pi,i))foriinrange(1,6)]['3.1', '3.14', '3.142', '3.1416', '3.14159']
>>> a=[66.25,333,333,1,1234.5]>>> printa.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]

5.2. The statement¶

There is a way to remove an item from a list given its index instead of its value: the statement. This differs from the method which returns a value. The 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:

can also be used to delete entire variables:

Referencing the name hereafter is an error (at least until another value is assigned to it). We’ll find other uses for later.

>>> a=[-1,1,66.25,333,333,1234.5]>>> dela[0]>>> a[1, 66.25, 333, 333, 1234.5]>>> dela[2:4]>>> a[1, 66.25, 1234.5]>>> dela[:]>>> a[]

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:

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 ). 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:

The statement is an example of tuple packing: the values , and are packed together in a tuple. The reverse operation is also possible:

This is called, appropriately enough, sequence unpacking and works for any sequence on the right-hand 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.

>>> 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]=88888Traceback (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])
>>> empty=()>>> singleton='hello',# <-- note trailing comma>>> len(empty)0>>> len(singleton)1>>> singleton('hello',)

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 function can be used to create sets. Note: to create an empty set you have to use , not ; the latter creates an empty dictionary, a data structure that we discuss in the next section.

Here is a brief demonstration:

Similarly to list comprehensions, set comprehensions are also supported:

>>> basket=['apple','orange','apple','pear','orange','banana']>>> fruit=set(basket)# create a set without duplicates>>> fruitset(['orange', 'pear', 'apple', 'banana'])>>> 'orange'infruit# fast membership testingTrue>>> 'crabgrass'infruitFalse>>> # Demonstrate set operations on unique letters from two words...>>> a=set('abracadabra')>>> b=set('alacazam')>>> a# unique letters in aset(['a', 'r', 'b', 'c', 'd'])>>> a-b# letters in a but not in bset(['r', 'd', 'b'])>>> a|b# letters in either a or bset(['a', 'c', 'r', 'd', 'b', 'm', 'z', 'l'])>>> a&b# letters in both a and bset(['a', 'c'])>>> a^b# letters in a or b but not bothset(['r', 'd', 'b', 'm', 'z', 'l'])
>>> a={xforxin'abracadabra'ifxnotin'abc'}>>> aset(['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 and .

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 . 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 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 function to it). To check whether a single key is in the dictionary, use the keyword.

Here is a small example using a dictionary:

The constructor builds dictionaries directly from sequences of key-value pairs:

In addition, dict comprehensions can be used to create dictionaries from arbitrary key and value expressions:

When the keys are simple strings, it is sometimes easier to specify pairs using keyword arguments:

>>> tel={'jack':4098,'sape':4139}>>> tel['guido']=4127>>> tel{'sape': 4139, 'guido': 4127, 'jack': 4098}>>> tel['jack']4098>>> deltel['sape']>>> tel['irv']=4127>>> tel{'guido': 4127, 'irv': 4127, 'jack': 4098}>>> tel.keys()['guido', 'irv', 'jack']>>> 'guido'intelTrue
>>> dict([('sape',4139),('guido',4127),('jack',4098)]){'sape': 4139, 'jack': 4098, 'guido': 4127}
>>> {x:x**2forxin(2,4,6)}{2: 4, 4: 16, 6: 36}
>>> 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 function.

To loop over two or more sequences at the same time, the entries can be paired with the function.

To loop over a sequence in reverse, first specify the sequence in a forward direction and then call the function.

To loop over a sequence in sorted order, use the function which returns a new sorted list while leaving the source unaltered.

When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the method.

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.

>>> fori,vinenumerate(['tic','tac','toe']):... printi,v...0 tic1 tac2 toe
>>> questions=['name','quest','favorite color']>>> answers=['lancelot','the holy grail','blue']>>> forq,ainzip(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.
>>> foriinreversed(xrange(1,10,2)):... printi...97531
>>> basket=['apple','orange','apple','pear','orange','banana']>>> forfinsorted(set(basket)):... printf...applebananaorangepear
>>> knights={'gallahad':'the pure','robin':'the brave'}>>> fork,vinknights.iteritems():... printk,v...gallahad the purerobin the brave
>>> importmath>>> raw_data=[56.2,float('NaN'),51.7,55.3,52.5,float('NaN'),47.8]>>> filtered_data=[]>>> forvalueinraw_data:... ifnotmath.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 and statements can contain any operators, not just comparisons.

The comparison operators and check whether a value occurs (does not occur) in a sequence. The operators and 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, tests whether is less than and moreover equals .

Comparisons may be combined using the Boolean operators and , and the outcome of a comparison (or of any other Boolean expression) may be negated with . These have lower priorities than comparison operators; between them, has the highest priority and the lowest, so that is equivalent to . As always, parentheses can be used to express the desired composition.

The Boolean operators and 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 and are true but is false, does not evaluate the expression . 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,

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.

>>> string1,string2,string3='','Trondheim','Hammer Dance'>>> non_null=string1orstring2orstring3>>> non_null'Trondheim'

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 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. [1] Mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc.



Python has a great built-in list type named "list". List literals are written within square brackets [ ]. Lists work similarly to strings -- use the len() function and square brackets [ ] to access data, with the first element at index 0. (See the official python.org list docs.)

colors = ['red', 'blue', 'green'] print colors[0] ## red print colors[2] ## green print len(colors) ## 3

Assignment with an = on lists does not make a copy. Instead, assignment makes the two variables point to the one list in memory.

b = colors ## Does not copy the list

The "empty list" is just an empty pair of brackets [ ]. The '+' works to append two lists, so [1, 2] + [3, 4] yields [1, 2, 3, 4] (this is just like + with strings).

FOR and IN

Python's *for* and *in* constructs are extremely useful, and the first use of them we'll see is with lists. The *for* construct -- -- is an easy way to look at each element in a list (or other collection). Do not add or remove from the list during iteration.

squares = [1, 4, 9, 16] sum = 0 for num in squares: sum += num print sum ## 30

If you know what sort of thing is in the list, use a variable name in the loop that captures that information such as "num", or "name", or "url". Since python code does not have other syntax to remind you of types, your variable names are a key way for you to keep straight what is going on.

The *in* construct on its own is an easy way to test if an element appears in a list (or other collection) -- -- tests if the value is in the collection, returning True/False.

list = ['larry', 'curly', 'moe'] if 'curly' in list: print 'yay'

The for/in constructs are very commonly used in Python code and work on data types other than list, so you should just memorize their syntax. You may have habits from other languages where you start manually iterating over a collection, where in Python you should just use for/in.

You can also use for/in to work on a string. The string acts like a list of its chars, so prints all the chars in a string.


The range(n) function yields the numbers 0, 1, ... n-1, and range(a, b) returns a, a+1, ... b-1 -- up to but not including the last number. The combination of the for-loop and the range() function allow you to build a traditional numeric for loop:

## print the numbers from 0 through 99 for i in range(100): print i

There is a variant xrange() which avoids the cost of building the whole list for performance sensitive cases (in Python 3000, range() will have the good performance behavior and you can forget about xrange()).

While Loop

Python also has the standard while-loop, and the *break* and *continue* statements work as in C++ and Java, altering the course of the innermost loop. The above for/in loops solves the common case of iterating over every element in a list, but the while loop gives you total control over the index numbers. Here's a while loop which accesses every 3rd element in a list:

## Access every 3rd element in a list i = 0 while i < len(a): print a[i] i = i + 3

List Methods

Here are some other common list methods.

  • list.append(elem) -- adds a single element to the end of the list. Common error: does not return the new list, just modifies the original.
  • list.insert(index, elem) -- inserts the element at the given index, shifting elements to the right.
  • list.extend(list2) adds the elements in list2 to the end of the list. Using + or += on a list is similar to using extend().
  • list.index(elem) -- searches for the given element from the start of the list and returns its index. Throws a ValueError if the element does not appear (use "in" to check without a ValueError).
  • list.remove(elem) -- searches for the first instance of the given element and removes it (throws ValueError if not present)
  • list.sort() -- sorts the list in place (does not return it). (The sorted() function shown below is preferred.)
  • list.reverse() -- reverses the list in place (does not return it)
  • list.pop(index) -- removes and returns the element at the given index. Returns the rightmost element if index is omitted (roughly the opposite of append()).

Notice that these are *methods* on a list object, while len() is a function that takes the list (or string or whatever) as an argument.

list = ['larry', 'curly', 'moe'] list.append('shemp') ## append elem at end list.insert(0, 'xxx') ## insert elem at index 0 list.extend(['yyy', 'zzz']) ## add list of elems at end print list ## ['xxx', 'larry', 'curly', 'moe', 'shemp', 'yyy', 'zzz'] print list.index('curly') ## 2 list.remove('curly') ## search and remove that element list.pop(1) ## removes and returns 'larry' print list ## ['xxx', 'moe', 'shemp', 'yyy', 'zzz']

Common error: note that the above methods do not *return* the modified list, they just modify the original list.

list = [1, 2, 3] print list.append(4) ## NO, does not work, append() returns None ## Correct pattern: list.append(4) print list ## [1, 2, 3, 4]

List Build Up

One common pattern is to start a list a the empty list [], then use append() or extend() to add elements to it:

list = [] ## Start as the empty list list.append('a') ## Use append() to add elements list.append('b')

List Slices

Slices work on lists just as with strings, and can also be used to change sub-parts of the list.

list = ['a', 'b', 'c', 'd'] print list[1:-1] ## ['b', 'c'] list[0:2] = 'z' ## replace ['a', 'b'] with ['z'] print list ## ['z', 'c', 'd']

Exercise: list1.py

To practice the material in this section, try the problems in list1.py that do not use sorting (in the Basic Exercises).

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