Editing Data structure (section)

==Implementation==
Data structures can be implemented using a variety of programming languages and techniques, but they all share the common goal of efficiently organizing and storing data.<ref>{{Cite journal |last1=Vaishnavi |first1=Gunjal |last2=Shraddha |first2=Gavane |last3=Yogeshwari |first3=Joshi |date=2021-06-21 |title=Survey Paper on Fine-Grained Facial Expression Recognition using Machine Learning |url=http://www.ijcaonline.org/archives/volume183/number11/vaishnavi-2021-ijca-921427.pdf |journal=International Journal of Computer Applications |volume=183 |issue=11 |pages=47–49 |doi=10.5120/ijca2021921427}}</ref> Data structures are generally based on the ability of a [[computer]] to fetch and store data at any place in its memory, specified by a [[pointer (computer programming)|pointer]]—a [[bit]] [[String (computer science)|string]], representing a [[memory address]], that can be itself stored in memory and manipulated by the program. Thus, the [[Array data structure|array]] and [[record (computer science)|record]] data structures are based on computing the addresses of data items with [[arithmetic operations]], while the [[linked data structure]]s are based on storing addresses of data items within the structure itself. This approach to data structuring has profound implications for the efficiency and scalability of algorithms. For instance, the contiguous memory allocation in arrays facilitates rapid access and modification operations, leading to optimized performance in sequential data processing scenarios.<ref>{{Citation |last1=Nievergelt |first1=Jürg |title=Chapter 17 - Spatial Data Structures: Concepts and Design Choices |date=2000-01-01 |url=https://www.sciencedirect.com/science/article/pii/B9780444825377500188 |work=Handbook of Computational Geometry |pages=725–764 |editor-last=Sack |editor-first=J. -R. |access-date=2023-11-12 |place=Amsterdam |publisher=North-Holland |isbn=978-0-444-82537-7 |last2=Widmayer |first2=Peter |editor2-last=Urrutia |editor2-first=J.}}</ref> 

The implementation of a data structure usually requires writing a set of [[subroutine|procedures]] that create and manipulate instances of that structure. The efficiency of a data structure cannot be analyzed separately from those operations. This observation motivates the theoretical concept of an [[abstract data type]], a data structure that is defined indirectly by the operations that may be performed on it, and the mathematical properties of those operations (including their space and time cost).<ref>{{Cite book|title=Advanced biotechnology : For B Sc and M Sc students of biotechnology and other biological sciences.|last=Dubey, R. C.|date=2014|publisher=S Chand|isbn=978-81-219-4290-4|location=New Delhi|oclc=883695533}}</ref>