Data structures organize and group data by type. The basic Python data structures are lists, sets, tuples, and dictionaries. Each has unique properties. Stacks implemented using lists follow first-in, last-out ordering and are useful for expressions, parentheses checking, and more. Lists allow accessing, inserting, and removing elements and are commonly used to represent stacks in Python code.
Data structure is an arrangement of data in computer's memory. It makes the data quickly available to the processor for required operations.It is a software artifact which allows data to be stored, organized and accessed.
Python _dataStructures_ List, Tuples, its functionsVidhyaB10
Is a group of data elements that are put together
under one name
Defines a particular way of storing and organizing
data in a computer so that it can be used efficiently
List
Tuples
This document provides an introduction to data structures and algorithms. It defines an algorithm as a set of steps to solve a problem and describes data structures as organized ways to store and access data. Common data structures include arrays, stacks, queues, linked lists, trees and graphs. Abstract data types define the fundamental operations on data objects in a data structure independently of their implementation. Linear data structures like arrays represent lists in one dimension while non-linear structures represent two-dimensional relationships. Stacks follow the last-in, first-out principle with push and pop operations, while queues are first-in, first-out. Selection of the appropriate data structure depends on the application.
The document discusses lists in Python. It begins by defining lists as mutable sequences that can contain elements of any data type. It describes how to create, access, manipulate, slice, traverse and delete elements from lists. It also explains various list methods such as append(), pop(), sort(), reverse() etc. and provides examples of their use. The document concludes by giving some programs on lists including finding the sum and maximum of a list, checking if a list is empty, cloning lists, checking for common members between lists and generating lists of square numbers.
This document discusses various common data structures, including their definitions, examples, and basic operations. It begins by defining an abstract data type and explaining that a data structure provides one way to implement an ADT by organizing data. Then it provides examples of common data structures like queues, stacks, binary search trees, lists, arrays, linked lists, graphs, and hashing. For each it briefly explains what it is, potential applications, and some basic operations that can be performed on it.
This document provides an overview of the course "Data Structures and Applications" including the module topics, definitions, and classifications of data structures. The first module covers introduction to data structures, including definitions of primitive and non-primitive data structures, data structure operations, arrays, structures, stacks, and queues. Key concepts like dynamic memory allocation and various data structure implementations are also summarized.
DS Complete notes for Computer science and EngineeringRAJASEKHARV8
The document provides information about data structures using C programming language. It discusses various topics like arrays, linked lists, stacks, queues, trees and graphs. It provides the syllabus, contents and references for the course on data structures. The document contains lecture notes on different data structure topics with examples and algorithms for common operations like search, insertion, deletion on arrays and linked lists.
This document provides an introduction to data structures. It defines data structures as representations of logical relationships between data elements. Data structures can be primitive, like integers and floats, or non-primitive, like lists, stacks, queues, trees and graphs. Non-primitive data structures are built from primitive structures and emphasize structuring groups of homogeneous or heterogeneous data. The document describes common data structures like arrays, lists, stacks, queues and trees, and explains their properties and implementations.
The document discusses different types of list data structures. It describes lists as sequential data structures that can be implemented as arrays or linked lists. Linked lists have nodes that are linked together via pointers. There are different types of linked lists including singly linked lists where each node has a next node pointer, circular lists where nodes form a ring, and doubly linked lists where each node has pointers to the next and previous nodes. The document provides examples and illustrations of implementing various list operations for each type of linked list.
This document discusses linear and non-linear data structures. Linear data structures like arrays, stacks, and queues store elements sequentially. Static linear structures like arrays have fixed sizes while dynamic structures like linked lists can grow and shrink. Non-linear structures like trees and graphs store elements in a hierarchical manner. Common abstract data types (ADTs) include stacks, queues, and lists, which define operations without specifying implementation. Lists can be implemented using arrays or linked lists.
The document outlines the key concepts of linked lists including:
- Linked lists allow for dynamic resizing and efficient insertion/deletion unlike arrays.
- A linked list contains nodes that have a data field and a pointer to the next node.
- Common operations on linked lists include insertion, deletion, searching, and traversing the list.
- The time complexity of these operations depends on whether it's at the head, tail, or interior node.
- Linked lists can be implemented using classes for the node and linked list objects.
This document provides an overview of Python data structures, focusing on lists and tuples. It discusses how lists and tuples store and organize data, how to define, access, update, and manipulate elements within lists and tuples using various Python functions and methods. Lists are described as mutable sequences that can contain elements of different data types, while tuples are described as immutable sequences. The document provides examples of using lists and tuples for tasks like stacks, queues, and storing records. It also covers list and tuple operations like slicing, filtering, mapping, and reducing.
The document discusses container data types in Python, including lists, tuples, sets, and dictionaries.
Lists allow indexing, slicing, and various methods like append(), insert(), pop(), and sort(). Tuples are like lists but immutable, and have methods like count(), index(), and tuple comprehension. Sets store unique elements and support operations like union and intersection. Dictionaries map keys to values and allow accessing elements via keys along with functions like get() and update().
This document provides an introduction to data structures and algorithms. It defines data structures as a way of organizing data that considers both the items stored and their relationship. Common data structures include stacks, queues, lists, trees, graphs, and tables. Data structures are classified as primitive or non-primitive based on how close the data items are to machine-level instructions. Linear data structures like arrays and linked lists store data in a sequence, while non-linear structures like trees and graphs do not rely on sequence. The document outlines several common data structures and their characteristics, as well as abstract data types, algorithms, and linear data structures like arrays. It provides examples of one-dimensional and two-dimensional arrays and how they are represented in
data structure details of types and .pptpoonamsngr
The document defines and describes various data structures. It begins by defining data structures as representations of logical relationships between data elements. It then discusses how data structures affect program design and how algorithms are paired with appropriate data structures. The document goes on to classify data structures as primitive and non-primitive, providing examples of each. It proceeds to describe several specific non-primitive data structures in more detail, including lists, stacks, queues, trees, and graphs.
The document discusses stacks, queues and linked lists. It provides details about the operations and implementations of each:
Stacks follow the LIFO principle and have push and pop operations. Queues follow the FIFO principle with insertion at the rear and deletion from the front.
Linked lists consist of nodes with data and links. They allow efficient insertion/removal anywhere through use of pointers. Linked lists can implement stacks and queues. Operations on linked lists include insertion, deletion, and traversal. Implementations of stacks and queues using linked lists are also provided through code examples.
The document provides an overview of common data structures including lists, stacks, queues, trees, and hash tables. It describes each data structure, how it can be implemented both statically and dynamically, and how to use the core Java classes like ArrayList, Stack, LinkedList, and HashMap that implement these structures. Key points covered include common operations for each structure, examples of using the Java classes, and applications like finding prime numbers in a range or matching brackets in an expression.
This document discusses linear data structures like lists, stacks, and queues. It describes how they can be implemented statically using arrays or dynamically using linked nodes. It specifically covers the List<T>, Stack<T>, and Queue<T> classes in C# which provide standard implementations of these data structures using resizable arrays. Examples are given of using the methods like Add, Push, Pop, Enqueue and Dequeue on the different data structures.
Slides for the presentation I gave at LambdaConf 2025.
In this presentation I address common problems that arise in complex software systems where even subject matter experts struggle to understand what a system is doing and what it's supposed to do.
The core solution presented is defining domain-specific languages (DSLs) that model business rules as data structures rather than imperative code. This approach offers three key benefits:
1. Constraining what operations are possible
2. Keeping documentation aligned with code through automatic generation
3. Making solutions consistent throug different interpreters
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DS Complete notes for Computer science and EngineeringRAJASEKHARV8
The document provides information about data structures using C programming language. It discusses various topics like arrays, linked lists, stacks, queues, trees and graphs. It provides the syllabus, contents and references for the course on data structures. The document contains lecture notes on different data structure topics with examples and algorithms for common operations like search, insertion, deletion on arrays and linked lists.
This document provides an introduction to data structures. It defines data structures as representations of logical relationships between data elements. Data structures can be primitive, like integers and floats, or non-primitive, like lists, stacks, queues, trees and graphs. Non-primitive data structures are built from primitive structures and emphasize structuring groups of homogeneous or heterogeneous data. The document describes common data structures like arrays, lists, stacks, queues and trees, and explains their properties and implementations.
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This document discusses linear and non-linear data structures. Linear data structures like arrays, stacks, and queues store elements sequentially. Static linear structures like arrays have fixed sizes while dynamic structures like linked lists can grow and shrink. Non-linear structures like trees and graphs store elements in a hierarchical manner. Common abstract data types (ADTs) include stacks, queues, and lists, which define operations without specifying implementation. Lists can be implemented using arrays or linked lists.
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- Linked lists allow for dynamic resizing and efficient insertion/deletion unlike arrays.
- A linked list contains nodes that have a data field and a pointer to the next node.
- Common operations on linked lists include insertion, deletion, searching, and traversing the list.
- The time complexity of these operations depends on whether it's at the head, tail, or interior node.
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This document provides an overview of Python data structures, focusing on lists and tuples. It discusses how lists and tuples store and organize data, how to define, access, update, and manipulate elements within lists and tuples using various Python functions and methods. Lists are described as mutable sequences that can contain elements of different data types, while tuples are described as immutable sequences. The document provides examples of using lists and tuples for tasks like stacks, queues, and storing records. It also covers list and tuple operations like slicing, filtering, mapping, and reducing.
The document discusses container data types in Python, including lists, tuples, sets, and dictionaries.
Lists allow indexing, slicing, and various methods like append(), insert(), pop(), and sort(). Tuples are like lists but immutable, and have methods like count(), index(), and tuple comprehension. Sets store unique elements and support operations like union and intersection. Dictionaries map keys to values and allow accessing elements via keys along with functions like get() and update().
This document provides an introduction to data structures and algorithms. It defines data structures as a way of organizing data that considers both the items stored and their relationship. Common data structures include stacks, queues, lists, trees, graphs, and tables. Data structures are classified as primitive or non-primitive based on how close the data items are to machine-level instructions. Linear data structures like arrays and linked lists store data in a sequence, while non-linear structures like trees and graphs do not rely on sequence. The document outlines several common data structures and their characteristics, as well as abstract data types, algorithms, and linear data structures like arrays. It provides examples of one-dimensional and two-dimensional arrays and how they are represented in
data structure details of types and .pptpoonamsngr
The document defines and describes various data structures. It begins by defining data structures as representations of logical relationships between data elements. It then discusses how data structures affect program design and how algorithms are paired with appropriate data structures. The document goes on to classify data structures as primitive and non-primitive, providing examples of each. It proceeds to describe several specific non-primitive data structures in more detail, including lists, stacks, queues, trees, and graphs.
The document discusses stacks, queues and linked lists. It provides details about the operations and implementations of each:
Stacks follow the LIFO principle and have push and pop operations. Queues follow the FIFO principle with insertion at the rear and deletion from the front.
Linked lists consist of nodes with data and links. They allow efficient insertion/removal anywhere through use of pointers. Linked lists can implement stacks and queues. Operations on linked lists include insertion, deletion, and traversal. Implementations of stacks and queues using linked lists are also provided through code examples.
The document provides an overview of common data structures including lists, stacks, queues, trees, and hash tables. It describes each data structure, how it can be implemented both statically and dynamically, and how to use the core Java classes like ArrayList, Stack, LinkedList, and HashMap that implement these structures. Key points covered include common operations for each structure, examples of using the Java classes, and applications like finding prime numbers in a range or matching brackets in an expression.
This document discusses linear data structures like lists, stacks, and queues. It describes how they can be implemented statically using arrays or dynamically using linked nodes. It specifically covers the List<T>, Stack<T>, and Queue<T> classes in C# which provide standard implementations of these data structures using resizable arrays. Examples are given of using the methods like Add, Push, Pop, Enqueue and Dequeue on the different data structures.
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2. Data-structures
It a way of organizing and storing data in such a manner so that it can be accessed
and work over it. This can be done efficiently and uses less resources are required. It
define the relationship between the data and the operations over those data. There
are various types of data structures defined that make it easier for the computer
programmer,to concentrate on the main problems rather than getting lost in the
details of data description and access.
Python Data Structure
3. List
It is a collections of items and each item has its own index value.
Index of first item is 0 and the last item is n-1.Here n is number of items in a list.
Indexing of list
Creating a list
Lists are enclosed in square brackets [ ] and each item is separated by a comma.
e.g.
list1 = [‘English', ‘Hindi', 1997, 2000];
list2 = [11, 22, 33, 44, 55 ];
list3 = ["a", "b", "c", "d"];
Data-structures
4. Access Items From A List
List items can be accessed using its index position.
output
e.g.
list =[3,5,9]
print(list[0])
print(list[1])
print(list[2])
print('Negativeindexing')
print(list[-1])
print(list[-2])
print(list[-3])
3
5
9
Negative indexing
9
5
3
Data-structures
5. Iterating Through A List
List elements can be accessed using looping statement.
e.g.
list =[3,5,9]
for i in range(0,len(list)):
print(list[i])
Output
3
5
9
Visit : python.mykvs.in for regular updates
Data-structures
6. Important methods and functions of List
Function
list.append()
list.extend()
list.insert()
list.remove()
del list[index]
list.clear()
list.pop()
list.index()
list.sort()
list.reverse()
len(list)
max(list)
min(list)
list(seq)
Description
Add an Item at end of a list
Add multiple Items at end of a list
insert an Item at a defined index
remove an Item from a list
Delete an Item from a list
empty all the list
Remove an Item at a defined index
Return index of first matched item
Sort the items of a list in ascending or descending order
Reverse the items of a list
Return total length of the list.
Return item with maximum value in the list.
Return item with min value in the list.
Converts a tuple, string, set, dictionary into
list.
Data-structures
7. Stack:
A stack is a linear data structure in which all the
insertion and deletion of data / values are done at one end only.
It is type of linear data structure.
It follows LIFO(Last In First Out)
property.
Insertion / Deletion in stack can
only be done from top.
Insertion in stack is also known as
a PUSH operation.
Deletion from stack is also known
as POP operation in stack.
Data-structures
8. Applications of Stack:
•
•
•
•
•
•
•
Expression Evaluation: It is used to evaluate prefix, postfix and infix
expressions.
Expression Conversion: It can be used to convert one form of
expression(prefix,postfix or infix) to one another.
Syntax Parsing: Many compilers use a stack for parsing the
syntax of
expressions.
Backtracking: It can be used for back traversal of steps in a
problem solution.
Parenthesis Checking: Stack is used to check the proper opening
and closing of parenthesis.
String Reversal: It can be used to reverse a string.
Function Call: Stack is used to keep information about the active
functions or subroutines.
Data-structures
9. Using List as Stack in Python:
The concept of Stack implementation is easy in Python ,
because it support inbuilt functions (append() and pop())
for stack implementation.By Using these functions make
the code short and simple for stack implementation.
To add an item to the top of the list, i.e., to push an item,
we use append() function and to pop out an element we
use pop() function. These functions work quiet efficiently
and fast in end operations.
Data-structures
11. class Stack:
def init
(self):
self.items = []
def is_empty(self):
return self.items == [] def
push(self, data):
self.items.append(data) def
pop(self):
return self.items.pop()
s = Stack() while
True:
print('Press 1 for push')
print('Press 2 for pop')
print('Press 3 for quit')
do = int(input('What would
you like to do'))
Stack interactive
program:
Data-structures
if do == 1:
n=int(input("enter a number to push")) s.push(n)
elif do == 2:
if s.is_empty(): print('Stack is empty.')
else:
print('Popped value: ', s.pop()) elif
operation ==
3: