In-built Python Data Structures
What are data structures?
Data structures refer to the way data is organized and stored in a computer’s memory. Different types of data structures are used to store different types of data and perform different types of operations on that data. The 4 in-built data structures in Python are List, Dictionary, tuple, and Sets. Let’s dive deeper into each of these.
In-built data structures in Python
- Lists
A list in Python is a collection of items that are ordered and changeable. The items in a list are enclosed in square brackets [], and each item is separated by a comma. Lists can contain items of different data types, including other lists. Lists are mutable, which means that their items can be changed after they are created. They are one of the most commonly used data structures in Python, and they have a wide range of uses, such as storing a collection of items, iterating through a collection, storing multiple pieces of related data, implementing data structures, sorting and searching, and using it as a buffer for data processing.
2. Dictionary
A dictionary in Python is a collection of key-value pairs, where each key is unique. Dictionaries are mutable and are implemented as hash tables. They are enclosed in curly braces {} and the items in a dictionary are separated by a comma. The key and the value are separated by a colon. The keys in a dictionary must be immutable, for example, strings and numbers can be used as keys but lists cannot be used as keys. Dictionaries are useful for storing and retrieving data that is associated with a specific key, such as configuration settings, word definitions, and other types of data where a unique identifier is needed to look up a value. They also can be used to implement a cache, where the key is the data being cached, and the value is the cached result. They are also useful for grouping data together by key, counting occurrences, and storing hierarchical data.
3. Tuple
A tuple in Python is a collection of items that are ordered and immutable. The items in a tuple are enclosed in parentheses () or separated by commas. Tuples can contain items of different data types, including other tuples. Once a tuple is created, the items inside it cannot be changed, unlike lists. Because of this, they are considered immutable data types. They are often used to store related pieces of data, such as the x and y coordinates of a point in a 2D space. Tuples are also used to represent a single record in a database, or a single row in a spreadsheet. They are also used in function and method calls to return multiple values. They are also useful in cases where you want to use certain data as a key for dictionaries, keys must be immutable.
4. Sets
A set in Python is an unordered collection of unique items. Sets are enclosed in curly braces {} and the items are separated by commas. The items in a set are not indexed, which means they cannot be accessed by an index like lists or tuples. Sets can contain items of different data types, including other sets. They are mutable, which means that their items can be changed after they are created.
One of the main use cases for sets is to quickly check for membership, i.e. to check if an item is in a set or not. It’s faster to check for membership in a set than in a list or a tuple because of the way sets are implemented.
They also can be used to remove duplicates from a list, perform mathematical set operations like union, intersection, and difference, and check if two sets have any common elements or not.
Why are data structures important?
Data structures are important because they provide a way to organize and store data in a way that is efficient for the specific problem that needs to be solved. The choice of data structure can greatly affect the performance of an algorithm and the amount of memory used. Here are a few reasons why data structures are important:
Efficiency: Different data structures have different time and space complexities for different operations. For example, searching for an element in a list takes O(n) time on average, while searching for an element in a hash table takes O(1) time on average. Choosing the right data structure can greatly improve the efficiency of an algorithm.
Memory usage: Some data structures are more memory-efficient than others. For example, a linked list uses less memory than an array to store the same amount of data.
Abstraction: Data structures provide an abstraction for the underlying data, allowing the programmer to focus on the logic of the algorithm rather than the details of how the data is stored.
Reusability: Many data structures are implemented as reusable libraries or classes, which can be used in different programs and projects.
Problem-solving: Different data structures are suited for different types of problems. For example, a stack data structure is well suited for problems that involve reverse order traversal, while a queue is well suited for problems that involve first-in-first-out traversal.
In summary, data structures are important because they provide a way to organize and store data in an efficient way, which can greatly improve the performance of an algorithm and make it easier to write code that is correct, maintainable, and reusable.
Author: Shivani Shankar
