Python Programming Tutorial
- 1: Getting started with Python Language – Part 1
- 1: Getting started with Python Language – Part 2
- 2: Python Data Types
- 3: Indentation in Python
- 4: Comments and Documentation in Python
- 5: Date and Time in Python
- 6: Date Formatting in Python
- 7: Enum in Python
- 8: Set in Python
- 9: Simple Mathematical Operators in Python
- 10: Bitwise Operators in Python
- 11: Boolean Operators in Python
- 12: Operator Precedence in Python
- 13: Variable Scope and Binding in Python
- 14: Conditionals statement in python
- 15: Comparisons operators in python
- 16: Loops in python
- 17: Arrays in python
- 18: Multidimensional arrays in Python
- 19: Dictionary in Python
- 20: List in Python
- 21: List comprehensions in Python
- 22: List slicing (selecting parts of lists) in Python
- 23: groupby() clause in Python
- 24: Linked lists in Python
- 25: Linked List Node in Python
- 26: Filter in Python
- 27: Heapq in Python
- 28: Tuple in Python
- 29: Basic Input and Output in Python
- 30: Files & Folders I/O in Python
- 31: os.path in Python
8: Set
Section 8.1: Operations on sets
with other sets
# Intersection |
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{1, 2, 3, 4, 5}.intersection({3, 4, 5, 6}) | # {3, 4, 5} | ||||
{1, 2, 3, 4, 5} & {3, | 4, 5, 6} |
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# Union |
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{1, 2, 3, 4, 5}.union({3, 4, 5, 6}) | # {1, 2, 3, 4, 5, 6} | ||||
{1, 2, 3, 4, 5} | {3, | 4, 5, 6} | # {1, 2, 3, 4, 5, 6} | |||
# Difference |
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{1, 2, 3, 4}.difference({2, 3, 5}) | # {1, 4} |
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{1, 2, 3, 4} – {2, 3, | 5} |
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# Symmetric difference with |
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{1, 2, 3, 4}.symmetric_difference({2, 3, 5}) | # {1, 4, 5} | ||||
{1, 2, 3, 4} ^ {2, 3, | 5} |
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# Superset check |
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{1, 2}.issuperset({1, | 2, 3}) # False |
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{1, 2} >= {1, 2, 3} |
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# Subset check |
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{1, 2}.issubset({1, 2, 3}) | # True |
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{1, 2} <= {1, 2, 3} |
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# Disjoint check |
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{1, 2}.isdisjoint({3, | 4}) | # True |
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{1, 2}.isdisjoint({1, | 4}) | # False |
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with single elements |
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# Existence check |
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2 in {1,2,3} | # True |
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4 in {1,2,3} | # False |
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4 not in {1,2,3} | # True |
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#Add and Removes = {1,2,3}
s.add(4) | # s == {1,2,3,4} | |
s.discard(3) | # s == {1,2,4} | |
s.discard(5) | # s == | {1,2,4} |
s.remove(2) | # s == | {1,4} |
s.remove(2) | # KeyError! |
Set operations return new sets, but have the corresponding in-place versions:
method | in-place operation | in-place method |
union | s |= t | update |
intersection | s &= t | intersection_update |
difference | s -= t | difference_update |
symmetric_difference s ^= tsymmetric_difference_update
For example:
s = {1, 2}
s.update({3, 4}) # s == {1, 2, 3, 4}
Section 8.2: Get the unique elements of a list
Let’s say you’ve got a list of restaurants — maybe you read it from a file. You care about the unique restaurants in the list. The best way to get the unique elements from a list is to turn it into a set:
restaurants = [“McDonald’s”, “Burger King”, “McDonald’s”, “Chicken Chicken”] unique_restaurants = set(restaurants)
print(unique_restaurants)
# prints {‘Chicken Chicken’, “McDonald’s”, ‘Burger King’}
Note that the set is not in the same order as the original list; that is because sets are unordered, just like dicts.
This can easily be transformed back into a List with Python’s built in list function, giving another list that is the same list as the original but without duplicates:
list(unique_restaurants)
# [‘Chicken Chicken’, “McDonald’s”, ‘Burger King’]
It’s also common to see this as one line:
#Removes all duplicates and returns another listlist(set(restaurants))
Now any operations that could be performed on the original list can be done again.
Section 8.3: Set of Sets
{{1,2}, {3,4}}
leads to:
TypeError: unhashable type: ‘set’
Instead, use frozenset:
{frozenset({1, 2}), frozenset({3, 4})}
Section 8.4: Set Operations using Methods and Builtins
We define two sets a and b
>>>a = {1, 2, 2, 3, 4}
>>>b = {3, 3, 4, 4, 5}
NOTE: {1} creates a set of one element, but {} creates an empty dict. The correct way to create an empty set is set().
Intersection
a.intersection(b) returns a new set with elements present in both a and b
>>>a.intersection(b) {3, 4}
Union
a.union(b) returns a new set with elements present in either a and b
>>>a.union(b) {1, 2, 3, 4, 5}
Difference
a.difference(b) returns a new set with elements present in a but not in b
>>>a.difference(b) {1, 2}
>>>b.difference(a)
{5}
Symmetric Difference
a.symmetric_difference(b) returns a new set with elements present in either a or b but not in both
>>>a.symmetric_difference(b) {1, 2, 5}
>>>b.symmetric_difference(a) {1, 2, 5}
NOTE: a.symmetric_difference(b) == b.symmetric_difference(a)
Subset and superset
c.issubset(a) tests whether each element of c is in a. a.issuperset(c) tests whether each element of c is in a.
>>>c = {1, 2}
>>>c.issubset(a)
True
>>>a.issuperset(c)
True
The latter operations have equivalent operators as shown below:
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a.intersection(b) | a | & b | ||||||||||||
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a.difference(b) | a – b | |||||||||||||
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a.symmetric_difference(b) | a ^ b | |||||||||||||
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a.issubset(b) | a <= b | |||||||||||||
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a.issuperset(b) | a >= b |
Disjoint sets
Sets a and d are disjoint if no element in a is also in d and vice versa.
>>>d = {5, 6}
>>>a.isdisjoint(b) # {2, 3, 4} are in both sets False
>>>a.isdisjoint(d)
True
#This is an equivalent check, but less efficient
>>> len(a & d) == 0 True
#This is even less efficient
>>>a & d == set()
True
Testing membership
The builtin in keyword searches for occurances
>>>1 in a
True
>>>6 in a False
Length
The builtin len() function returns the number of elements in the set
>>>len(a)
4
>>>len(b)
3
Section 8.5: Sets versus multisets
Sets are unordered collections of distinct elements. But sometimes we want to work with unordered collections of elements that are not necessarily distinct and keep track of the elements’ multiplicities.
Consider this example:
>>>setA = {‘a’,‘b’,‘b’,‘c’}
>>>setA
set([‘a’, ‘c’, ‘b’])
By saving the strings ‘a’, ‘b’, ‘b’, ‘c’ into a set data structure we’ve lost the information on the fact that ‘b’ occurs twice. Of course saving the elements to a list would retain this information
>>>listA = [‘a’,‘b’,‘b’,‘c’]
>>>listA
[‘a’, ‘b’, ‘b’, ‘c’]
but a list data structure introduces an extra unneeded ordering that will slow down our computations.
For implementing multisets Python provides the Counter class from the collections module (starting from version 2.7):
Python 2.x Version ≥ 2.7
>>>from collections import Counter
>>>counterA = Counter([‘a’,‘b’,‘b’,‘c’])
>>>counterA
Counter({‘b’: 2, ‘a’: 1, ‘c’: 1})
Counter is a dictionary where where elements are stored as dictionary keys and their counts are stored as dictionary values. And as all dictionaries, it is an unordered collection.
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*This content is compiled from Stack Overflow Documentation, and the content is written by the beautiful people at Stack Overflow. This work is licensed under cc by-sa.