Since Python 2.6, the types set and frozenset are built-in types. A set is an unordered collection of objects, unlike sequence objects such as lists and tuples, in which each element is indexed. Sets cannot have duplicate members - a given object appears in a set 0 or 1 times. All members of a set have to be hashable, just like dictionary keys. Integers, floating point numbers, tuples, and strings are hashable; dictionaries, lists, and other sets (except frozensets) are not.

### OverviewEdit

Sets in Python at a glance:

set1 = set() # A new empty set set1.add("cat") # Add a single member set1.update(["dog", "mouse"]) # Add several members if "cat" in set1: # Membership test set1.remove("cat") #set1.remove("elephant") - throws an error print set1 for item in set1: # Iteration AKA for each element print item print "Item count:", len(set1) # Length AKA size AKA item count isempty = len(set1) == 0 # Test for emptiness set1 = set(["cat", "dog"]) # Initialize set from a list set2 = set(["dog", "mouse"]) set3 = set1 & set2 # Intersection set4 = set1 | set2 # Union set5 = set1 - set3 # Set difference set6 = set1 ^ set2 # Symmetric difference issubset = set1 <= set2 # Subset test issuperset = set1 >= set2 # Superset test set7 = set1.copy() # A shallow copy set7.remove("cat") set8 = set1.copy() set8.clear() # Clear AKA empty AKA erase print set1, set2, set3, set4, set5, set6, set7, set8, issubset, issuperset

### Constructing SetsEdit

One way to construct sets is by passing any sequential object to the "set" constructor.

>>> set([0, 1, 2, 3]) set([0, 1, 2, 3]) >>> set("obtuse") set(['b', 'e', 'o', 's', 'u', 't'])

We can also add elements to sets one by one, using the "add" function.

>>> s = set([12, 26, 54]) >>> s.add(32) >>> s set([32, 26, 12, 54])

Note that since a set does not contain duplicate elements, if we add one of the members of s to s again, the add function will have no effect. This same behavior occurs in the "update" function, which adds a group of elements to a set.

>>> s.update([26, 12, 9, 14]) >>> s set([32, 9, 12, 14, 54, 26])

Note that you can give any type of sequential structure, or even another set, to the update function, regardless of what structure was used to initialize the set.

The set function also provides a copy constructor. However, remember that the copy constructor will copy the set, but not the individual elements.

>>> s2 = s.copy() >>> s2 set([32, 9, 12, 14, 54, 26])

### Membership TestingEdit

We can check if an object is in the set using the same "in" operator as with sequential data types.

>>> 32 in s True >>> 6 in s False >>> 6 not in s True

We can also test the membership of entire sets. Given two sets and , we check if is a subset or a superset of .

>>> s.issubset(set([32, 8, 9, 12, 14, -4, 54, 26, 19])) True >>> s.issuperset(set([9, 12])) True

Note that "issubset" and "issuperset" can also accept sequential data types as arguments

>>> s.issuperset([32, 9]) True

Note that the <= and >= operators also express the issubset and issuperset functions respectively.

>>> set([4, 5, 7]) <= set([4, 5, 7, 9]) True >>> set([9, 12, 15]) >= set([9, 12]) True

Like lists, tuples, and string, we can use the "len" function to find the number of items in a set.

### Removing ItemsEdit

There are three functions which remove individual items from a set, called pop, remove, and discard. The first, pop, simply removes an item from the set. Note that there is no defined behavior as to which element it chooses to remove.

>>> s = set([1,2,3,4,5,6]) >>> s.pop() 1 >>> s set([2,3,4,5,6])

We also have the "remove" function to remove a specified element.

>>> s.remove(3) >>> s set([2,4,5,6])

However, removing a item which isn't in the set causes an error.

>>> s.remove(9) Traceback (most recent call last): File "<stdin>", line 1, in ? KeyError: 9

If you wish to avoid this error, use "discard." It has the same functionality as remove, but will simply do nothing if the element isn't in the set

We also have another operation for removing elements from a set, clear, which simply removes all elements from the set.

>>> s.clear() >>> s set([])

### Iteration Over SetsEdit

We can also have a loop move over each of the items in a set. However, since sets are unordered, it is undefined which order the iteration will follow.

>>> s = set("blerg") >>> for n in s: ... print n, ... r b e l g

### Set OperationsEdit

Python allows us to perform all the standard mathematical set operations, using members of set. Note that each of these set operations has several forms. One of these forms, s1.function(s2) will return another set which is created by "function" applied to and . The other form, s1.function_update(s2), will change to be the set created by "function" of and . Finally, some functions have equivalent special operators. For example, s1 & s2 is equivalent to s1.intersection(s2)

#### IntersectionEdit

Any element which is in both and will appear in their intersection.

>>> s1 = set([4, 6, 9]) >>> s2 = set([1, 6, 8]) >>> s1.intersection(s2) set([6]) >>> s1 & s2 set([6]) >>> s1.intersection_update(s2) >>> s1 set([6])

#### UnionEdit

The union is the merger of two sets. Any element in or will appear in their union.

>>> s1 = set([4, 6, 9]) >>> s2 = set([1, 6, 8]) >>> s1.union(s2) set([1, 4, 6, 8, 9]) >>> s1 | s2 set([1, 4, 6, 8, 9])

Note that union's update function is simply "update" above.

#### Symmetric DifferenceEdit

The symmetric difference of two sets is the set of elements which are in one of either set, but not in both.

>>> s1 = set([4, 6, 9]) >>> s2 = set([1, 6, 8]) >>> s1.symmetric_difference(s2) set([8, 1, 4, 9]) >>> s1 ^ s2 set([8, 1, 4, 9]) >>> s1.symmetric_difference_update(s2) >>> s1 set([8, 1, 4, 9])

#### Set DifferenceEdit

Python can also find the set difference of and , which is the elements that are in but not in .

>>> s1 = set([4, 6, 9]) >>> s2 = set([1, 6, 8]) >>> s1.difference(s2) set([9, 4]) >>> s1 - s2 set([9, 4]) >>> s1.difference_update(s2) >>> s1 set([9, 4])

### Multiple setsEdit

Starting with Python 2.6, "union", "intersection", and "difference" can work with multiple input by using the set constructor. For example, using "set.intersection()":

>>> s1 = set([3, 6, 7, 9]) >>> s2 = set([6, 7, 9, 10]) >>> s3 = set([7, 9, 10, 11]) >>> set.intersection(s1, s2, s3) set([9, 7])

### frozensetEdit

A frozenset is basically the same as a set, except that it is immutable - once it is created, its members cannot be changed. Since they are immutable, they are also hashable, which means that frozensets can be used as members in other sets and as dictionary keys. frozensets have the same functions as normal sets, except none of the functions that change the contents (update, remove, pop, etc.) are available.

>>> fs = frozenset([2, 3, 4]) >>> s1 = set([fs, 4, 5, 6]) >>> s1 set([4, frozenset([2, 3, 4]), 6, 5]) >>> fs.intersection(s1) frozenset([4]) >>> fs.add(6) Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'frozenset' object has no attribute 'add'

### ExercisesEdit

- Create the set {'cat', 1, 2, 3}, call it s.
- Create the set {'c', 'a', 't', '1', '2', '3'}.
- Create the frozen set {'cat', 1, 2, 3}, call it fs.
- Create a set containing the frozenset fs, it should look like {frozenset({'cat', 2, 3, 1})}.

### ReferenceEdit

- Python Tutorial, section "Data Structures", subsection "Sets" -- python.org
- Python Library Reference on Set Types -- python.org