There is no one-size-fits-all kind of model when it comes to data structures.You have multiples algorithms, the steps of which require fetching the smallest value in a collection at any given point of time. Values are assigned to variables but are constantly modified, making it impossible for you to remember all the changes.

You have multiples algorithms, the steps of which require fetching the smallest value in a collection at any given point of time. Values are assigned to variables but are constantly modified, making it impossible for you to remember all the changes. One way to work through this problem is to store this collection in an unsorted array and then scan this collection every time, to find the required value. But considering the collection has N elements, this would lead to an increase in the required amount of time proportional to N.

Data structures to the rescue! Let us invent a common operation that ‘finds the minimum value from a set of elements.’ Here, the data structure is the common operation that all these algorithms will make use of to find the minimum element much faster.

There is no one single way of looking up data. Hence, when using an algorithm, make sure to understand the kind of data structures used by it and the operations they are a part of. The main purpose of a data structure is to speed up operations. In the above example, when I talk about an unsorted array, that too is a data structure. If the algorithm you are working with doesn’t care about a quicker result, you could continue using the array to get results.

In case a data structure is what your algorithm requires, time must be spent on designing and maintaining one so that it becomes easier to query and update the structure.

Data structures provide us with a specific and way of storing and organizing data such that they can be easily accessed and worked with efficiently. In this article, you will learn about the various Python data structures and how they are implemented.

A link to my GitHub repository to access the Jupyter notebook used for this demonstration:

Contribute to sowmya20/DataStructures_Intro development by creating an account on GitHub.

Broadly speaking, data structures can be classified into two types — primitive and non-primitive. The former is the basic way of representing data which contain simple values. The latter is a more advanced and a complex way off representing data that contain a collection of values in various formats.

🔵 Intellipaat Data Science with Python course: https://intellipaat.com/python-for-data-science-training/In this Data Science With Python Training video, you...

🔥Intellipaat Python for Data Science Course: https://intellipaat.com/python-for-data-science-training/In this python for data science video you will learn e...

Master Applied Data Science with Python and get noticed by the top Hiring Companies with IgmGuru's Data Science with Python Certification Program. Enroll Now

Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.