Python - Set

> Procedural Languages > Python > Python - Data Type

1 - About

Implementation of a set data structure in Python.

  • Sets are mutable. There is a non-mutable version of set called frozenset.
  • The elements of a set are not mutable. A set then cannot contain a list since lists are mutable.
  • A set cannot have a set as element.
  • A set doesn't allow duplicates
  • The number elements of a set are ordered
Advertising

3 - Initialization

You can use curly braces to give an expression whose value is a set.

>>> {1+2,3,'a'}
{3, 'a'}

The empty set is represented by set() and not by {} (which is a dictionary)

>>> x={}
>>> type(x)
<class 'dict'>

The duplicates are eliminated

Python prints sets using curly braces.

>>> {4,5,3}
{3, 4, 5}

The order in which the elements of the output are printed does not necessarily match the order of the input elements.

3.1 - Constructor

>>> set(range(10))
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}
>>> set([1,2,3])
{1, 2, 3}
>>> set((1,2,3))
{1, 2, 3}
Advertising

4 - Function

4.1 - Length

The cardinality of a set S is the number of elements in the set.

In Python, the cardinality of a set is obtained using the procedure len().

>>> len({3,4,5})
3

4.2 - Sum

  • Sum beginning at 0
>>> sum({3,4,5})
12

* Sum beginning at 10

>>> sum({3,4,5},10)
22

5 - Comparator

5.1 - In

>>> S={1,2,3}
>>> 1 in S
True
>>> 1 not in S
False

5.2 - Equality

>>> S1={1,2,3}
>>> S2={1,2,3}
>>> S1==S2
True

6 - Operation

6.1 - union

>>> {1,2,3} | {4,5,6}
{1, 2, 3, 4, 5, 6}

6.2 - intersection

>>> {1,2,3} & {3,4,5}
{3}

7 - Mutation

7.1 - Add

>>> S={1,2,3}
>>> S.add(4)
>>> S
{1, 2, 3, 4}

The add method must not be used in a sub expression but apart

Advertising

7.2 - Remove

>>> S={1,2,3}
>>> S.remove(2)
>>> S
{1, 3}

7.3 - Update

Add to a set all the elements of another collection (e.g. a set or a list)

>>> S
{1, 3}
>>> S.update({2})
>>> S
{1, 2, 3}

7.4 - Intersection Update

Intersect a set with another collection, removing from the set all elements not in the other collection.

>>> S
{1, 2, 3}
>>> S.intersection_update({1,2,5,7})
>>> S
{1, 2}

7.5 - Copy

>>> S
{1, 2}
>>> S2= S.copy()
>>> S2
{1, 2}
>>> S2.add(3)
>>> S2
{1, 2, 3}
>>> S
{1, 2}
>>> S
{1, 2}
>>> S2=S
# After executing the assignment statement S2=S, both S2 and S point 
# to the same data structure (same address in memory)
>>> S2.remove(2)
>>> S
{1}

8 - Comprehension

8.1 - Introduction

Python provides expressions called comprehensions that let you build collections out of other collections.

They are useful in constructing an expression whose value is a collection, and they mimic traditional mathematical notation.

>>> {2*x for x in {1,2,3} }
{2, 4, 6}

It's a set comprehension over the set {1,2,3}. It is called a set comprehension because its value is a set.

The notation is similar to the traditional mathematical notation for expressing sets in terms of other sets, in this case:

<math>\{2x : x \in \{1, 2, 3\}\}</math>

8.2 - With an union or intersection

You can sue the union operator | or the intersection operator & in a comprehension:

>>> {x*x for x in {1,2} | {3, 4}}
{16, 1, 4, 9}

8.3 - With filtering

By adding a if condition (a filtering condition), you can skip some of the values in the set being iterated over:

>>> {str(x)+' is greater than 2' for x in {1, 2, 3, 4} if x>2}
{'4 is greater than 2', '3 is greater than 2'}

8.4 - Double comprehension

You can write a comprehension that iterates over the Cartesian product of two sets.

Example:

  • The set of the products of every combination of x and y.
>>> {x*y for x in {1,2} for y in {1,2,3}}
{1, 2, 3, 4, 6}
  • A double comprehension with a filter:
>>> {x*y for x in {1,2,3} for y in {2,3,4} if x != y}
{2, 3, 4, 6, 8, 12}