Python is one of the most flexible programming languages that is used universally for many different fields including information processing, web development, and data science. To make efficient and optimized programs, you must have a solid understanding of basic Python Data Structures. One such powerful data structure is Python Sets, which is known for its unique properties and functions to solve complex Python Problems.
With the Python Sets tutorial, you will learn everything about sets in Python, including creation, manipulation, and usage, with examples to make your learning easier.
Table of Contents:
What are Sets in Python
A Set in Python is generally an unordered and unique collection of elements that is used to store multiple items into a single variable. Sets are majorly used to perform various set operations like union, intersection, difference, and symmetric difference. Sets are basically unordered, unique, and mutable in nature which means that they can be modified after initialization. Sets typically provide efficient data storage, fast lookup, and convenient set operations that make it a perfect choice for handling large data sets, removing duplicates, and multiple data validation operations.
One of the major advantages of using sets in Python is that, unlike some other data types like Python Lists, sets contain a highly optimized method for the sole purpose of checking whether a particular element is included in a set or not.
Also, since sets in Python are mutable, we can add and remove elements from a set; however, every element that is added to the set must be unique and immutable, that is, we cannot change elements once they have been added.
Key Features of Sets in Python
- Unique Elements: Python sets make their elements unique, and any duplicates in them will be removed automatically.
- Unordered Collection: Python sets store items without any defined order, in contrast with lists and tuples, whose items maintain sequence.
- No Indexing and Slicing: As sets lack any order, slicing operations and accessing the elements of the set with indexing are not supported.
- Mutable Data Structure: Due to its mutablilitySets in Python can dynamically be modified at run-time, and items can be added and removed.
How to Create a Set in Python
There are majorly two methods to create a set in Python, one is simply using curly braces and separated commas and with the built-in set() Method.
1. Curly Braces
Sets can be created by using curly braces and writing elements separated by commas.
Output:
2. set() Method
Using the built-in set() method with the elements that we want to add given as the parameters.
Output:
Remember that once a set is created we can’t change that set. We can only add elements. We can remove and then add elements but cannot change the existing elements. Now, let us see how to add items in a Python set.
Adding Elements to a Set in Python
Following are some of the methods for adding elements to a set in Python:
1. Using add() Method in Python
Using the add() method with the element as the parameter:
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2. Using update() Method in Python
Using update() method with new elements as parameters.
Output:
Removing Elements from Sets in Python
Remove operation can be performed by using the following methods:
1. Using the remove() method
Output:
Note: If the item to be removed does not exist, remove() will raise an error.
2. Using discard()
Output:
Note: If the item to be removed does not exist, discard() will not raise an error.
3. Using pop():
Remember that pop() will remove the last item of a set. Since sets are unordered, we should avoid performing pop() in sets.
Output:
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How to Find the Length of a Set in Python
To print the length of a set, we can simply use the in-built len() method as shown below:
Output:
Emptying a Python Set Completely
To clear a set completely, we can use either the clear() method or the del() method.
1. Using clear() method
Output:
2. Using del() method
Output:
This error arises because the original set was deleted successfully.
Python Set operations
As discussed above, sets in Python are used to carry out mathematical set operations such as union, intersection, etc. Let’s see a few examples of these mathematical operations.
1. Sets Union
To perform union, we use “|” operator. We can also use an in-built method called union(), which will give the same result. The result of the union of two sets, say Set A and Set B, is a set containing the elements of both sets.
The following code block shows the union of Set A and Set B:
Output:
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2. Set Intersection
To perform an intersection, we use the ‘&’ operator. We can also use the in-built Python Function named intersection() to get the same result.
The intersection of two sets say Set A and Set B, can result in a set that contains the elements that are common in both sets, that is, the overlapping part in the diagram below.
The following code block shows the union of Set A and Set B:
Output:
Python Set Functions
When working with data in Python, choosing the right data structure is essential for efficient coding. Sets, Lists, and Tuples each have distinct characteristics that suit different use cases. Sets are best for unique elements and fast operations, lists provide flexibility with ordered data, and tuples offer immutability for fixed collections.
The table below highlights the key differences to help you decide when to use each:
| Function |
Description |
| add() |
Adds an element to the set if it’s not already there. |
| clear() |
Removes all elements from the set. |
| copy() |
Creates a copy of the set for independent use. |
| difference() |
Returns elements present in the first set but not in the other sets. |
| intersection() |
Returns common elements shared by multiple sets. |
| isdisjoint() |
Check if the two sets have no common elements. |
| issubset() |
Checks if all elements in one set are found in another set. |
| issuperset() |
Check if the current set contains all elements of another set. |
| remove() |
Removes a specified element. Raises an error if it doesn’t exist. |
| symmetric_difference() |
Returns elements that are unique to each set, excluding common ones. |
| union() |
Combines all elements from two or more sets, excluding duplicates. |
| update() |
Adds elements from another set or iterable to the current set. |
Frozenset in Python
frozenset() is an inbuilt Python function that takes an iterable object as input and makes it immutable. It simply freezes the iterable objects and makes them unchangeable. The frozenset() function returns an unchangeable frozenset object (which is like a set object, only unchangeable).
In Python, frozenset() is the same as set except the forzensets are immutable which means that elements from the frozenset cannot be added or removed once created. This function takes input as any iterable object and converts it into an immutable object. The order of elements is not guaranteed to be preserved.
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Handling Nested Sets with Frozenset
Regular sets in Python cannot contain other sets as elements since sets are mutable and unhashable. However, frozenset allows you to create immutable sets that can be nested within other sets.
Example:
Output:
This feature is particularly useful in scenarios where you need hierarchical or nested collections.
Python Ordered Set
There aren’t any ordered sets in Python, but programmers can use collections. OrderedDict from the Python standard library with just keys and the values as none.
Output:
Set Comprehension in Python
Set comprehensions in Python generally provide an efficient and simpler approach to create or initialize Sets. They allow you to construct sets by filtering and transforming data in a single line of code.
Example:
Output:
This is particularly useful when you need to efficiently create sets from other iterable data.
Memory Efficiency of Sets Compared to Lists and Tuples
Python sets have a lot of memory efficiency when working with big collections of individual items. Sets utilize hashing, in contrast to lists, and therefore search, insert, and delete operations become efficient.
Example Benchmark:
Output:
This shows that sets take more storage than lists due to their hash table implementation. By using sets for operations involving uniqueness and quick lookups, developers can achieve better performance and lower memory consumption.
Concurrency and Thread-Safety in Sets
Python sets are not thread-safe, and concurrent access and updates can introduce race conditions. To address concurrency concerns, use a mechanism for synchronization, for example, lock objects in Python’s threading module.
Example:
Output:
Chaining Set Operations
With Chaining of Set operations, you can simply combine the multiple set operations in a single line of code in order to make code clean and efficient for data manipulation.
Example:
Output:
Sets vs Lists vs Tuples in Python
While working with a variety of datasets in Python, choosing an ideal data structure for efficient development is very important. Sets, lists, and tuples are the types of data structures that most frequently contribute to resolving complex data-related problems. Here we have discussed the key differences between Sets, Lists, and Tuples that generally help you to choose the required data structures.
| Feature |
Set |
List |
Tuple |
| Ordering |
Unordered (no guaranteed order) |
Ordered |
Ordered |
| Duplicates Allowed |
No |
Yes |
Yes |
| Mutable/Immutable |
Mutable (elements immutable) |
Mutable |
Immutable |
| Access Method |
Cannot access by index |
Indexed (supports slicing) |
Indexed (supports slicing) |
| Performance |
Faster for membership tests |
Slower for search operations |
Faster than lists for read operations |
| Usage |
Ideal for unique elements and set operations |
Ideal for dynamic, ordered collections |
Ideal for fixed, ordered collections |
| Syntax Example |
{1, 2, 3} |
[1, 2, 3] |
(1, 2, 3) |
| Hashable Elements |
Yes, all elements must be hashable |
No |
No |
| Method Support |
Set-specific operations (union(), intersection()) |
List-specific (append(), pop()) |
Tuple-specific (count(), index()) |
| Use Case |
Best for removing duplicates, set operations |
Best for flexible, ordered data |
Best for immutable, structured data |
How to Convert List to Set in Python
If you want to convert a list to a Set in Python, you need to remove the duplicates from the original list. Also, you can perform operations like union, intersections, and differences.
Example:
Output
How to Convert Set to List in Python
To convert a set to a list in Python, you can typecast using the list(set_name) method. Type Casting to list can be done by simply using list(set_name).
Example:
Output:
Real-World Use Cases of Sets in Python
- Removes Duplicates: Sets in Python help majorly in filtering out duplicate values which makes it useful in the data cleaning process.
- Faster Membership Testing: It checks whether an element exists in a particular set and this process is comparatively faster than a list due to hashing in Sets.
- Graph Algorithms Optimization: They are also used in various algorithms like depth-first search(DFS) and breadth-first search(BFS) in order to track the visited nodes.
- Data Comparison: These Sets are also used to perform different comparison operations like union, intersection, and difference for quick dataset comparison in the field of data analysis.
- Managing User Permission: Sets in Python also help in managing the unique user roles and access permissions in the applications
Best Practices When Using Sets in Python
- Always use sets whenever you are working with unique and unordered data.
- Try to avoid modifying the sets during the iteration, instead make sure to iterate over a copy to prevent any errors.
- As the set removes the duplicates, it can lead to data loss whenever you need to try to convert a list to sets, so you need to do it cautiously.
- Always be careful of the time complexity of set operations as operations like issubset() can still take more time depending on the size of the set.
- Whenever you need to store the sets inside the other sets or you want to use them as dictionary keys, then use the frozen set.
Conclusion
In this Python Sets Tutorial, you have learned Python Sets starting from its creation till working with several operations over it. You have also learned its numerous inbuilt methods and applications that help in solving multiple complex Python problems. To make your Python code optimized and cleaner knowing when and how to use sets in Python can make a significant impact. Python’s immutability feature also helps in working dynamically which helps in maintaining integrity and consistency in your data. Hence, mastering Python Sets helps to make one capable of solving a range of Python-related questions with ease.
FAQs on Python Sets
1. What is a set in Python, and how is it different from lists and tuples?
A set in Python is an unsorted group of unique items, in contrast with lists, which maintain a specific sequence, and tuples, which cannot be changed. Sets work best when duplicates have to be eliminated and searching must be efficient.
2. How do I create a set in Python?
You can create a set with curly braces { } or with a function set(). For example, my_set = {1, 2, 3} will produce a three-element set.
3. What are the key operations you can perform with Python sets?
Common operations involve union (|), intersection (&), difference (-), and symmetric difference (^). All these operations are useful in math and filtering out data.
4. Can a Python set contain other sets?
No, regular sets cannot have sets in them, for sets are unhashable and can change. But a frozen set, an immutable alternative, can be used in sets for nested collections.
5. What are a few real-life use cases for sets in Python?
Sets are used for operations including filtering duplicates in large datasets, storing one-time IDs for access controls, and comparing two or more datasets for similar items in an efficient manner.
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