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data structures

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Data structures are the ways in which data is organized and stored in a computer. They're used to store and manage data in an effective and systematized manner, allowing for quick and easy access, manipulation, and reclamation of data.

There are several common data structures in computer wisdom, including

Arrays An array is a collection of rudiments, each linked by an indicator or a key. In Python, arrays can be enforced using the list data type.

Linked lists A linked list is a data structure that consists of a chain of bumps, each of which contains a value and a reference to the coming knot in the list. Linked lists can be used to apply dynamic arrays, heaps, and ranges.

heaps A mound is a data structure that follows the Last In First eschewal( LIFO) principle. It can be used to apply colorful algorithms, similar to depth-first hunt, and to estimate expressions.

ranges A line is a data structure that follows the First In First Out( FIFO) principle. It can be used to apply algorithms similar to breadth-first hunt and to manage tasks in a first-come, first-served manner.

Trees A tree is a hierarchical data structure that consists of bumps connected by edges. There are several types of trees, including double trees, AVL trees, and B- trees, each of which has different parcels and uses.

Hash tables A hash table is a data structure that uses a hash function to collude keys to indicators in an array. Hash tables can be used to apply wordbooks and sets and are frequently used for searching and sorting data.

Graphs A graph is a data structure that consists of a set of vertices connected by edges. Graphs can be used to represent connections between objects and to apply algorithms similar as shortest path algorithms and network inflow algorithms.

The choice of data structure depends on the problem being answered, the quantum of data to be stored, and the operations that need to be performed on the data. The effectiveness of data structures depends on their time complexity, which is the quantum of time it takes to perform colorful operations on the data, similar to fitting, deleting, or searching for rudiments.