An in-depth guide to Python Data Structures with advantages, types, and Data Structure operations with examples:
Data Structures are the set of data elements that produce a well-organized way of storing and organizing the data in the computer so it can be used well. For example, the data structures like Stack, Queue, Linked List, etc.
Data Structures are mostly used in the field of Computer Science, Artificial intelligence Graphics, etc. They play a very interesting role in the life of programmers to store and play with the data in a systematic order while working with dynamic large projects.
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Table of Contents:
Data Structures In Python
Data Structures Algorithms increase the production/execution of the software and a program, that are used to store and get back the user’s related data.
Basic Terminology
Data Structures act as the roots of large programs or software. The most difficult situation for a developer or a programmer is to select the specific data structures which are efficient for the program or a problem.
Given below are some terminologies that are used nowadays:
Data: It can be described as a group of values. For example, “Student’s name”, “Student’s id”, “Student’s roll no”, etc.
Group Items: The data items which are further subdivided into parts are known as group items. For example, “Student Name” is divided into three parts “First Name”, “Middle Name” and “Last Name”.
Record: It can be described as a group of various data elements. For example, if we talk about a particular company, then its “Name”, “Address”, “Area of knowledge of a company”, “Courses”, etc are combined together to form a record.
File: A file can be described as a group of records. For example, in a company, there are various departments, “Sales departments”, “Marketing departments”, etc. These departments have a number of employees working together. Each department has a record of each employee which will be stored as a record.
Now, there will be a file for each department in which all the records of employees are getting saved together.
Attribute and Entity: Let’s understand this with an example!
| Name | Roll no | Subject |
|---|---|---|
| Kanika | 9742912 | Physics |
| Manisha | 8536438 | Mathematics |
In the above example, we have a record that stores the names of the students along with their roll number and subjects. If you see, we store the names, roll no and the subjects of the students under the “Names”, “Roll no” and “Subject” columns and fill the rest of the row with the required information.
The attribute is the column that stores the information related to the particular name of the column. For example, “Name = Kanika” here the attribute is “Name” and “Kanika” is an entity.
In short, the columns are the attributes and the rows are the entities.
Field: It is a single unit of information that represents the attribute of an entity.
Let’s understand it with a diagram.
Need For Data Structures
We need data structures nowadays because things are becoming complex and the amount of data is increasing at a high rate.
Processor Speed: Data is increasing day by day. To handle a large amount of data, high-speed processors are needed. Sometimes processors fail while dealing with huge amounts of data.
Data Search: With the increase of data on a daily basis it becomes difficult to search and find the particular data from the huge amount of data.
For example, what if we need to search the one item from the 1000 items? Without data structures, the result will take time to traverse each item from 1000 items and will find the result. To overcome this, we need data structures.
Multiple Requests: Sometimes multiple users are finding the data on the webserver which slows down the server and the user does not get the result. To resolve this issue, data structures are used.
They organize the data in a well-organized manner so that the user can find the searched data in a minimum time without slowing down the servers.
Advantages Of Data Structures
- Data Structures enable the storage of information on hard disks.
- They help to manage large data sets for example databases, internet indexing services, etc.
- Data Structures play an important role when someone wants to design algorithms.
- Data Structures secure the data and can’t be lost. One can use the stored data in multiple projects and programs.
- It processes the data easily.
- One can access the data anytime anywhere from the connected machine, for example, a computer, laptop, etc.
Python Data Structure Operations
The following operations play an important role in terms of Data Structures:
- Traversing: It means to traverse or visit each element of the particular data structure only once so that the elements can be processed.
- For example, we need to calculate the sum of the weights of each node in the graph. We will traverse each element (weight) of an array one by one to perform the addition of weights.
- Searching: It means to find/locate the element in the data structure.
- For example, we have an array, let say “arr = [2,5,3,7,5,9,1]”. From this, we need to find the location of “5”. How do we find it?
- Data Structures provide various techniques for this situation and some of them are Linear search, Binary search, etc.
- Inserting: It means to insert the data elements in the data structure at any time and anywhere.
- Deleting: It means to delete the elements in the data structures.
- Sorting: Sorting means sorting/arranging the data elements either in ascending order or descending order. Data Structures provides various sorting techniques, for example, insertion sort, quick sort, selection sort, bubble sort, etc.
- Merging: It means merging the data elements.
- For example, there are two lists “L1” and “L2” with their elements. We want to combine/merge them into one “L1 + L2”. Data Structures provide the technique to perform this merge sort.
Types Of Data Structures
Data Structures are divided into two parts:
#1) Built-in Data Structures
Python provides various data structures that are written in Python itself. These data structures help the developers to ease their work and obtain the output very fast.
Given below are some Built-in Data Structures:
- List: Lists are used to reserve/store the data of various data types in a subsequent way. Every element of the list has an address which we can call the index of an element. It starts from 0 and ends at the last element. For notation, it is like ( 0, n-1 ). It supports negative indexing as well which begins from -1 and we can traverse the elements from end to start. To make this concept clearer you can refer to this List Tutorial
- Tuple: Tuples are the same as lists. The main difference is that the data present in the list can be changed but the data present in tuples can not be changed. It can be changed when the data in the tuple is mutable. Check this Tuple Tutorial for more information on Tuple.
- Dictionary: Dictionaries in Python contain unordered information and are used to store the data in pairs. Dictionaries are case-sensitive in nature. Each element has its key value. For example, in a school or college, each student has his/her unique roll number. Each roll number has just only one name which means the roll number will act as a key and the student roll number will act as the value to that key. Refer to this link for more information on Python Dictionary
- Set: Set contains unordered elements which are unique. It does not include the elements in repetition. Even if the user adds one element twice, then it would be added to the set only once. Sets are unchangeable as if they are created once and cannot be changed. Not possible to delete the elements but adding the new elements is possible.
#2) User-Defined Data Structures
Python supports user-defined data structures i.e. the user can create their own data structures, for example, Stack, Queue, Tree, Linked List, Graph, and Hash Map.
- Stack: Stack works on the concept of Last-In-First-Out (LIFO) and is a linear data structure. The data that is stored at the last element of the stack will pull out first and the element which gets stored at first will pull out at last. The operations of this data structure are push and pop, whereas push means to add the element to the stack and pop means to delete the elements from the stack. It has a TOP that acts as a pointer and points to the current position of the stack. Stacks are mainly used while performing the recursion in the programs, reversing words, etc.
- Queue: Queue works on the concept of First-In-First-Out (FIFO) and again is a linear data structure. The data stored first will come out first and the data stored the last will come out at the last turn.
- Tree: Tree is the user-defined data structure that works on the concept of trees in nature. This data structure starts from the up and goes down with its branches/nodes. It is the combination of nodes and edges. Nodes are connected with the edges. The nodes that are at the bottom are known as leaf nodes. It does not have any cycle.
- Linked List: Linked List is the order of data elements, which are connected together with the links. One of all elements in the linked list has the connection to the other elements as a pointer. In Python, the linked list is not present in the standard library. Users can implement this data structure using the idea of nodes.
- Graph: A graph is an illustrative representation of a group of objects where a few pairs of objects are joined by the links. The inter-relationship objects are constituted by the points known as vertices and the links that join these vertices are known as edges.
- Hash Map: The hash map is the data structure that matches the key with its value pairs. It uses a hash function to evaluate the index value of the key in the bucket or slot. Hash tables are used to store the key values and those keys are generated using the hash functions.
Frequently Asked Questions
Q #1) Is Python good for Data Structures?
Answer: Yes, the data structures in Python are more versatile. Python has many built-in data structures as compared to other programming languages. For example, List, Tuple, Dictionary, etc makes it more impressive and makes it a perfect fit for beginners who want to play with data structures.
Q #2) Should I learn data structures in C or Python?
Answer: It depends on the individual capabilities. Basically, data structures are used to store the data in a well-organized manner. All the things will be the same in the data structures in both languages but, the only difference is the syntax of each programming language.
Q #3) What are basic data structures?
Answer: Basic data structures are Arrays, Pointers, Linked List, Stacks, Trees, Graphs, Hash maps, queues, Searching, Sorting, etc
Conclusion
In the above tutorial, we learn about the data structures in Python. We have learned the types and sub-types of each data structure in brief.
The below topics were covered here in this tutorial:
- Introduction to data structures
- Basic Terminology
- Need for data structures
- Advantages of data structures
- Data structure operations
- Types of data structures
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