A Brief Guide to Python data structures

A Brief Guide to Python data structures

Introduction to Data Structures

What is a data structure? As the name indicates, it is the process of organizing, processing, or storing the data in a desired format. It is the process of building a structure to store the data that is generated.

In data structures, there are multiple types, which are divided based on the arrangement and linearity given for the data. The types of data structures will be explained shortly.

Suggested reading for Data Structures: Data Structures Interview Questions

 

What is Python Data Structure?

Python is one of the most widely used, high-level, general-purpose, open-source, object-oriented programming languages.

Python data structures are the containers that help in organizing and grouping the data while storing it according to its data type. Python data structures differ with change in order. These data structures are also responsible for handling the data in the proper format.

The Python coding platform is very reliable due to its significant indentation. It does not only support the object-oriented paradigm but also the structured and functional programming paradigms.\

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Types of data structure in Python

The Python data structures are much more reliable to store and access data. A specific type or desired data structure shall be used based on the functionality to be implemented in the data.

Python data structures aid in the maintenance of a specific method of data organization based on the case your project follows. This helps in maintaining a proper organized format of data to make it more understandable to the programmers and to help the programs load faster.

The following is a detailed explanation of each type of Python data structure.

 

Built-in Python Data Structure:

As the name suggests, built-in data structures are the type of python data structures that are built-in with python. This helps programmers use these functionalities to work on projects more reliably.

These non-primitive data structures help store data in multiple formats. This allows programmers to hold or load multiple values of different formats in random locations or in continuous locations.

The following are four non-primitive Python data structures:

 

- List sequentially store data of various types. The addresses assigned to elements of the list masks up a table-type structure. This address is called the index. The index begins at zero and rises until it reaches the final element, known as the positive index. Negative indices start at -1, which allows access to elements from last to first.

- Dictionary is the collection of unordered data that is stored with addresses of key value pairs. The indexing of the Python dictionary is done with keys. These key values are provided into the dictionary to make it more organized and optimized to the users. Unlike other data structures holding a single element as value, dictionary holds a pair of Key:Value.

- Tuple is basically a type of Python data structure, the same as a list, in which the elements are immutable. This means that the elements in the tuple cannot be replaced or altered. Just as a list, a tuple can store multiple elements of multiple data types.

- Set is the type of element that collects data in an unordered sequence. Though it is mutable, it does not allow duplication of elements. This feature allows programs to remove duplication in data.

 

User-Defined Python Data Structure:

To simply put it, "user-defined data structures" are a collection of data types that are defined by the user in the initial stage of coding.

A user-defined Python data structure allows programmers to create a data type for a specific part of the program. This can later be used throughout the program.

 Python data structures 2

- Stack is a user-defined data structure that stores data in a Last-In-First-Out (LIFO) manner. In a stack, insertion and deletion are only done from one end, which is generally the top. Stacks in Python are also called "Last Come, First Serve" data structures since the element inserted at the end is served and removed first.

- Queues store data in the order "First In, First Out" (FIFO). In a Python queue, the insertion is done at one end while the deletion is done at the other. A queue is also called a "First Come, First Serve" data structure, since the element inserted first is served first and is removed.

- Trees are Python data structures that are defined by a collection of elements called nodes. These nodes link together and represent the hierarchy of each node.

- Linked Lists is another user-defined Python data structure, which is a linear structure of collection of nodes. Each node in a linked list contains data about the element or entity along with a reference to the next node. The first node is called the Head Node, and it helps in accessing the entire Linked List. The last node is called the Tail Node, and it represents the end of the Lined List.

- Graphs are non-linear Python data structures that connect nodes to other nodes in the graph. Such nodes are called vertices. Multiple vertices connected together are called edges. These edges represent the relationships between various nodes in the graph.

- HashMaps also known as HashTable, are one of the Python data structures that tracks the keys of its value pairs. HashMaps compute the index values of the pair that stores the elements that are to be searched. The key values are generated using hash functions. Unlike the dictionary data structure, HashMaps are non-linear, and the order of elements can be changed.

 

Conclusion

This guide clarifies the types of Python data structures and their applications in the Python programming language.

Data structures are important to manage and store the data in an organized manner to help provide a reliable platform for programmers to access.

Data structures help in maintaining systematic data collection, which helps in quick loading and easy accessing of stored data.

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Sushmith

Our technical content writer, Sushmith, is an experienced writer, creating articles and content for websites, specializing in the areas of training programs and educational content. His writings are mainly concerned with the most major developments in specialized certification and training, e-learning, and other significant areas in the field of education.

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