Everand Logo

Welcome to Everand!

  • Discover millions of ebooks, audiobooks, and so much more with a free trial

    From $11.99/month after trial. Cancel anytime.

    Data Structures and Program Design Using Python: A Self-Teaching Introduction to Data Structures and Python
    Data Structures and Program Design Using Python: A Self-Teaching Introduction to Data Structures and Python
    Data Structures and Program Design Using Python: A Self-Teaching Introduction to Data Structures and Python
    Ebook674 pages4 hours

    Data Structures and Program Design Using Python: A Self-Teaching Introduction to Data Structures and Python

    By Mercury Learning and Information, D. Malhotra and N. Malhotra

    ()

    Read preview

    About this ebook

    This book, part of the Pocket Primer series, introduces the basic concepts of data science using Python 3 and other applications. It offers a fast-paced introduction to data analytics, statistics, data visualization, linear algebra, and regular expressions. The book features numerous code samples using Python, NumPy, R, SQL, NoSQL, and Pandas. Companion files with source code and color figures are available.
    Understanding data science is crucial in today's data-driven world. This book provides a comprehensive introduction, covering key areas such as Python 3, data visualization, and statistical concepts. The practical code samples and hands-on approach make it ideal for beginners and those looking to enhance their skills.
    The journey begins with working with data, followed by an introduction to probability, statistics, and linear algebra. It then delves into Python, NumPy, Pandas, R, regular expressions, and SQL/NoSQL, concluding with data visualization techniques. This structured approach ensures a solid foundation in data science.

    LanguageEnglish
    PublisherPackt Publishing
    Release dateAug 1, 2024
    ISBN9781836640721
    Data Structures and Program Design Using Python: A Self-Teaching Introduction to Data Structures and Python

    Read more from Mercury Learning And Information

    Related to Data Structures and Program Design Using Python

    Related ebooks

    Programming For You

    View More
    View More

    Related podcast episodes

    Related articles

    • UNLIMITED

      Exclusive Downloads

      May 22, 2023

      FREE FULL SOFTWARE FOR APC READERS If the system boots up more and more slowly and the hard drive gets fuller, then it is high time for a thorough system cleaning! But which data can be deleted without risk, which are still needed for the smooth sys

      2 min read

    • UNLIMITED

      Switch To Private Versions Of Software

      Jan 18, 2023

      The code for open-source software is made freely available to other developers, so they can help improve programs by identifying bugs and suggesting new features. This collaboration often involves ‘forking’, which allows you to modify somebody else’s

      5 min read

    • UNLIMITED

      Exclusive Software

      Feb 24, 2020

      Exclusive Software
      1 min read

    • UNLIMITED

      Best Free Downloads For 2023

      Dec 21, 2022

      • Remove hidden PC junk using Microsoft’s new clean-up tool • Back up files and drives without paying for premium options • Try Thunderbird’s new Android app before anyone else • Legally download thousands of classic books for free • Download high-qu

      16 min read

    • UNLIMITED

      Exclusive Downloads

      Apr 20, 2023

      Backup and restore files extensively in just a few clicks! If you’ve ever stared at a black screen because your Windows refused to boot, you’ll appreciate the value of regular backups. And it can be so easy! Only backups will protect you against impe

      2 min read

    • UNLIMITED

      3d Animation Basics 06

      Jul 17, 2019

      3d Animation Basics 06
      3 min read

    • UNLIMITED

      Exclusive Downloads

      Apr 1, 2024

      Download these APC exclusives free from: www.apcmag.com/exclusives SYSTEM CLEANING AND TUNING FOR WINDOWS. A total of six helpers analyse your system and enable deep cleaning operations. For example, you can delete temporary files, clean browser hist

      2 min read

    • UNLIMITED

      Exclusive Downloads

      Nov 6, 2023

      Download these APC exclusives free from: www.apcmag.com/exclusives AUTO-CROP AND EDIT OBJECTS AND SWAP BACKGROUNDS. Ashampoo Background Remover crops persons and objects automatically thanks to smart object detection – no manual editing required in m

      2 min read

    • UNLIMITED

      Unreal Engine 5.2

      Sep 5, 2023

      Unreal Engine 5.2
      3 min read

    • UNLIMITED

      Exclusive Downloads

      Aug 19, 2024

      Download these APC exclusives free from: www.apcmag.com/exclusives Discover Screencapt, the powerful Windows software for screen recording that allows you to accurately and easily record any activity on your screen. Whether you want to create a tutor

      2 min read

    • UNLIMITED

      Block All Tools That Track You

      Jun 18, 2024

      All the main web browsers now include tracking protection, though some (Brave and Firefox) do a better job than others (Chrome and Edge) through their ‘strict’ or ‘aggressive’ settings. However, to stay completely private online by blocking every hid

      5 min read

    • UNLIMITED

      Best Antivirus Software

      Mar 3, 2021

      Best Antivirus Software
      14 min read

    • UNLIMITED

      The Best Of The Freebies

      Jun 14, 2021

      The Best Of The Freebies
      15 min read

    • UNLIMITED

      Mission Center

      Oct 17, 2023

      Mission Center
      1 min read

    • UNLIMITED

      Asuswrt Merlin vs Netgear Nighthawk

      Nov 9, 2021

      ROUTERS HAVE COME a long way in the past 20 years, each year brings better features, new designs, and bigger wireless ranges. But for the enthusiast, a good router stretches beyond just “how quick is it?” And quickly moves on to “how easy is it to co

      4 min read

    • UNLIMITED

      The Best Of The Freebies

      May 25, 2021

      The Best Of The Freebies
      15 min read

    • UNLIMITED

      Exclusive Downloads

      Feb 22, 2021

      Exclusive Downloads
      2 min read

    • UNLIMITED

      Browser Tips

      Jul 20, 2022

      Websites automatically detect your location from your IP address so they can deliver relevant content, but they can also use this data to identify and track you. You can use a VPN (see our Cover Feature in Issue 631, page 50) to disguise this informa

      3 min read

    • UNLIMITED

      One Tree To Rule Them All

      Apr 19, 2022

      One Tree To Rule Them All
      7 min read

    • UNLIMITED

      Avira Prime for Windows

      May 3, 2023

      To provide thorough protection, today’s security products must do more than just scan for viruses and malware. They need to check for harmful Internet traffic, intrusive browser tracking, privacy issues, outdated software, and weak passwords, among o

      4 min read

    • UNLIMITED

      Exclusive Downloads

      Jun 14, 2021

      Exclusive Downloads
      2 min read

    • UNLIMITED

      Best Free Software

      Jun 21, 2023

      SYSTEM INFORMATION TOOL www.snipca.com/46385 What you need: Windows 10 or 11 Windows doesn’t make it easy to find information about your PC, such as the make of your processor, the amount of available storage space or which version of the operating s

      3 min read

    Related categories

    Reviews for Data Structures and Program Design Using Python

    0 ratings

    0 ratings0 reviews

    What did you think?

    Tap to rate

    Review must be at least 10 words

      Book preview

      Data Structures and Program Design Using Python - Mercury Learning and Information

      CHAPTER 1

      INTRODUCTION TO DATA STRUCTURES

      1.1 INTRODUCTION

      A data structure is an efficient way of storing and organizing the data elements in a computer’s memory. Data means a value or a collection of values. Structure refers to a method of organizing the data. The mathematical or logical representation of data in the memory is referred to as a data structure. The objective of a data structure is to store, retrieve, and update the data efficiently. A data structure can be considered as all the elements grouped under one name. The data elements are called members, and they can be of different types. Data structures are used in almost every program and software system. There are various kinds of data structures that are suited for different types of applications. Data structures are the building blocks of a program. For a program to run efficiently, a programmer must choose the appropriate data structures. A data structure is a crucial part of data management. As the name suggests, data management is a task that includes different activities, like the collection of data and the organization of data into structures. Data structures are used in stacks, queues, arrays, binary trees, linked lists, and hash tables.

      A data structure helps us to understand the relationship of one element to another element and organize it within the memory. It is a mathematical or logical representation or organization of data in the memory. Data structures are extensively applied in the following areas:

      •Compiler Design

      •Database Management Systems (DBMS)

      •Artificial Intelligence

      •Network and Numerical Analysis

      •Statistical Analysis Packages

      •Graphics

      •Operating Systems (OS)

      •Simulations

      There are many applications in which different data structures are used for their operations. Some data structures sacrifice speed for the efficient utilization of memory, while others sacrifice memory utilization and result in a faster speed. In today’s world, programmers aim not just to build a program, but to build an effective program. As previously discussed, for a program to be efficient, a programmer must choose the appropriate data structures. Hence, data structures are classified into various types. Now, let us discuss and learn about different types of data structures.

      Frequently Asked Questions

      1. Define the term data structure.

      Answer:

      A data structure is an organization of data in a computer’s memory or disk storage. In other words, a logical or mathematical model of a particular organization of data is called a data structure. A data structure in computer science is also a way of storing data in a computer so that it can be used efficiently. An appropriate data structure allows a variety of important operations to be performed using both resources, that is, the memory space and execution time, efficiently.

      1.2 TYPES OF DATA STRUCTURES

      Data structures are classified into various types.

      1.2.1 Linear and Non-Linear Data Structures

      A linear data structure is one in which the data elements are stored in a linear, or sequential, order; that is, data is stored in consecutive memory locations. A linear data structure can be represented in two ways; either it is represented by a linear relationship between various elements utilizing consecutive memory locations as in the case of arrays, or it may be represented by a linear relationship between the elements utilizing links from one element to another as in the case of linked lists. Examples of linear data structures include arrays, linked lists, stacks, and queues.

      A non-linear data structure is one in which the data is not stored in any sequential order or consecutive memory locations. The data elements in this structure are represented by a hierarchical order. Examples of non-linear data structures include graphs and trees.

      1.2.2 Static and Dynamic Data Structures

      A static data structure is a collection of data in memory that is fixed in size and cannot be changed during runtime. The memory size must be known in advance, as the memory cannot be reallocated later in a program. One example is an array.

      A dynamic data structure is a collection of data in which memory can be reallocated during the execution of a program. The programmer can add or remove elements according to his/her need. Examples include linked lists, graphs, and trees.

      1.2.3 Homogeneous and Non-Homogeneous Data Structures

      A homogeneous data structure is one that contains data elements of the same type (for example, arrays).

      A non-homogeneous data structure contains data elements of different types (for example, structures).

      1.2.4 Primitive and Non-Primitive Data Structures

      Primitive data structures are the fundamental data structures or predefined data structures that are supported by a programming language. Examples of primitive data structure types are integer, float, and char.

      Non-primitive data structures are comparatively more complicated data structures that are created using primitive data structures. Examples of non-primitive data structures are arrays, files, linked lists, stacks, and queues.

      The classification of different data structures is shown in Figure 1.1.

      FIGURE 1.1 Classification of different data structures

      Python supports various data structures. We now introduce all these data structures, and they are discussed in detail in the upcoming chapters.

      Frequently Asked Questions

      2. What is the difference between primitive data structures and non-primitive data structures?

      Answer:

      The data structures that are typically directly operated upon by machine-level instructions, that is, the fundamental data types such as int, float, and char, are known as primitive data structures. The data structures that are not fundamental are called non-primitive data structures.

      Frequently Asked Questions

      3. What is the difference between linear and non-linear data structures?

      Answer:

      The main difference between linear and non-linear data structures lies in the way in which data elements are organized. In the linear data structure, elements are organized sequentially, and therefore they are easy to implement in a computer’s memory. In non-linear data structures, a data element can be attached to several other data elements to represent specific relationships existing among them.

      1.2.5 Arrays/Lists

      The array structure looks very similar to Python’s list structure. That’s because the two structures are both sequences that are composed of multiple sequential elements that can be accessed by position. But there are two major differences between the array and the list. First, an array has a limited number of operations, which commonly include those for array creation, reading a value from a specific element, and writing a value to a specific element. The list provides a large number of operations for working with the content of the list. Second, the list can grow and shrink during execution as elements are added or removed while the size of an array cannot be changed after it has been created.

      Python’s list structure is a mutable sequence container that can change size as items are added or removed. It is an abstract data type that is implemented using an array structure to store the items contained in the list.

      In Python, a list is declared using the following syntax:

      Syntax – pyList = [4, 12, 2, 34, 17]

      1.2.6 Stacks

      A stack is a collection of objects that are inserted and removed according to the Last-In, First-Out (LIFO) principle. A user may insert objects into a stack at any time, but may only access or remove the most recently inserted object that remains (at the so-called top of the stack). The name stack is derived from the metaphor of a stack of plates in a spring-loaded, cafeteria plate dispenser. In this case, the fundamental operations involve the pushing and popping of plates on the stack. When we need a new plate from the dispenser, we pop the top plate off the stack, and when we add a plate, we push it down on the stack to become the new top plate.

      Practical Application:

      A real-life example of a stack is a pile of plates arranged on a table.

      A person will pick up the first plate from the top of the stack.

      The Stack ADT can be implemented in several ways. The two most common approaches to implement Stack ADT in Python include the use of a Python list and a linked list. The choice depends on the type of application involved.

      1.2.7 Queues

      Another fundamental data structure is the queue. It is a close cousin of the stack, as a queue is a collection of objects that are inserted and removed according to the First-In, First-Out (FIFO) principle. That is, elements can be inserted at any time, but only the element that has been in the queue the longest can be next removed. We usually say that elements enter a queue at the back and are removed from the front. A metaphor for this terminology is a line of people waiting to get on an amusement park ride. People waiting for such a ride enter at the back of the line and get on the ride from the front of the line.

      Practical Application:

      For a simple illustration of a queue, imagine there is a line of people standing at the bus stop and waiting for the bus. The first person standing in the line will get into the bus first.

      The Queue ADT can be implemented in several ways. The two most common approaches in Python include the use of a Python list and a linked list. The choice depends on the type of application involved.

      1.2.8 Linked Lists

      The major drawback of the array is that the size or the number of elements must be known in advance. Thus, this drawback gave rise to the new concept of a linked list. A linked list is a linear collection of data elements. These data elements are called nodes, which store the address of the next node. A linked list is a sequence of nodes in which each node contains one or more than one data field and an address field that stores the address of the next node. Linked lists are dynamic; that is, memory is allocated when required.

      FIGURE 1.2 Memory representation of a linked list

      Figure 1.2 shows a linked list in which each node is divided into two slots:

      1. The first slot contains information/data.

      2. The second slot contains the address of the next node.

      Practical Application:

      A simple real-life example is a train; here, each train car is connected to the previous one and next one (except the first car (the engine) and the last car (the coach)).

      The address part of the last node stores a special value called NULL, which denotes the end of the linked list. The advantage of a linked list over arrays is that now it is easier to insert and delete data elements, as we don’t have to do shifting each time. Yet searching for an element is difficult. More time is required to search for an element, and it requires a large amount of memory space. Hence, linked lists are used where a collection of data elements is required but the number of data elements in the collection is not known to us in advance.

      Frequently Asked Questions

      4. Define the term linked list.

      Answer:

      A linked list or one-way list is a linear collection of data elements called nodes, which give a linear order. It is a popular dynamic data structure. The nodes in the linked list are not stored in consecutive memory locations. For every data item in a node of the linked list, there is an associated address field that gives the address location of the next node in the linked list.

      1.2.9 Trees

      A tree is a popular non-linear data structure in which the data elements or the nodes are represented in a hierarchical order. Here, one of the nodes is shown as the root node of the tree, and the remaining nodes are partitioned into two disjointed sets such that each set is a part of a sub-tree. A tree makes the search process very easy, and its recursive programming makes a program optimized and easy to understand.

      A binary tree is the simplest form of a tree. A binary tree consists of a root node and two sub-trees known as the left sub-tree and the right sub-tree, where both sub-trees are also binary trees. Each node in a tree consists of three parts, that is, the extreme left part stores the address of the left sub-tree, the middle part consists of the data element, and the extreme right part stores the address of the right sub-tree. The root is the topmost element of the tree. When there are no nodes in a tree, that is, when ROOT = NULL, then it is called an empty tree.

      For example, consider a binary tree where R is the root node of the tree. LEFT and RIGHT are the left and right sub-trees of R, respectively. Node A is designated as the root node of the tree. Nodes B and C are the left and right child of A, respectively. Nodes B, D, E, and G constitute the left sub-tree of the root. Similarly, nodes C, F, H, and I constitute the right sub-tree of the root.

      FIGURE 1.3 A binary tree

      Advantages of a tree

      1. The searching process is very fast in trees.

      2. The insertion and deletion of the elements is easier compared to other data structures.

      Frequently Asked Questions

      5. Define the term binary tree.

      Answer:

      A binary tree is a hierarchal data structure in which each node has at most two children, that is, the left and right child. In a binary tree, the degree of each node can be at most two. Binary trees are used to implement binary search trees, which are used for efficient searching and sorting. A variation of BST is an AVL tree, where the height of the left and right subtree differs by one. A binary tree is a popular subtype of a k-ary tree, where k is 2.

      1.2.10 Graphs

      A graph is a general tree with no parent-child relationship. It is a non-linear data structure that consists of vertices, also called nodes, and the edges that connect those vertices. In a graph, any complex relationship can exist. A graph G may be defined as a finite set of V vertices and E edges. Therefore, G = (V, E) where V is the set of vertices and E is the set of edges. Graphs are used in various applications of mathematics and computer science. Unlike a root node in trees, graphs don’t have root nodes; rather, the nodes can be connected to any node in the graph. Two nodes are called neighbors when they are connected via an edge.

      Practical Application:

      A real-life example of a graph can be seen in workstations where several computers are joined to one another via network connections.

      For example, consider a graph G with six vertices and eight edges. Here, Q and Z are neighbors of P. Similarly, R and T are neighbors of S.

      FIGURE 1.4 A graph

      1.3 OPERATIONS ON DATA STRUCTURES

      Here, we discuss various operations that are performed on data structures.

      •Creation – This is the process of creating a data structure. The declaration and initialization of the data structure are done here. It is the first operation.

      •Insertion – This is the process of adding new data elements in the data structure, for example, to add the details of an employee who has recently joined an organization.

      •Deletion – This is the process of removing a particular data element from the given collection of data elements, for example, to remove the name of an employee who has left the company.

      •Updating – This is the process of modifying the data elements of a data structure. For example, if the address of a student is changed, it should be updated.

      •Searching – This is used to find the location of a particular data element or all the data elements with the help of a given key, for example, to find the names of people who live in New York.

      •Sorting – This is the process of arranging the data elements in some order, that is, either ascending or descending order. An example is arranging the names of students of a class in alphabetical order.

      •Merging – This is the process of combining the data elements of two different lists to form a single list of data elements.

      •Traversal – This is the process of accessing each data element exactly once so that it can be processed. An example is to print the names of all the students of a class.

      •Destruction – This is the process of deleting the entire data structure. It is the last operation in the data structure.

      1.4 ALGORITHMS

      An algorithm is a systematic set of instructions combined to solve a complex problem. It is a step-by-finite-step sequence of instructions, each of which has a clear meaning and can be executed in a minimum amount of effort in finite time. In general, an algorithm is a blueprint for writing a program to solve the problem. Once we have a blueprint of the solution, we can easily implement it in any high-level language like C, C++, or Python. It divides the problem into a finite number of steps. An algorithm written in a programming language is known as a program. A computer is a machine with no brain or intelligence. Therefore, the computer must be instructed to perform a given task in unambiguous steps. Hence, a programmer must define his problem in the form of an algorithm written in English. Thus, such an algorithm should have the following features:

      1. An algorithm should be simple and concise.

      2. It should be efficient and effective.

      3. It should be free of ambiguity; that is, the logic must be clear.

      Similarly, an algorithm must have the following characteristics:

      •Input – It reads the data of the given problem.

      •Output – The desired result must be produced.

      •Process/Definiteness – Each step or instruction must be unambiguous.

      •Effectiveness – Each step should be accurate and concise. The desired result should be produced within a finite time.

      •Finiteness – The number of steps should be finite.

      1.4.1 Developing an Algorithm

      To develop an algorithm, some steps are necessary:

      1. Defining or understanding the problem.

      2. Identifying the result or output of the problem.

      3. Identifying the inputs required by the problem and choosing the best input.

      4. Designing the logic from the given inputs to get the desired output.

      5. Testing the algorithm for different inputs.

      6. Repeating the previous steps until it produces the desired result for all the inputs.

      1.5 APPROACHES FOR DESIGNING AN ALGORITHM

      A complicated algorithm is divided into smaller units called modules. These modules are further divided into sub-modules. Thus, in this way, a complex algorithm can easily be solved. The process of dividing an algorithm into modules is called modularization. There are two popular approaches for designing an algorithm:

      •Top-Down Approach

      •Bottom-Up Approach

      Now let us understand both approaches.

      1. Top-Down Approach –A top-down approach states that the complex/complicated problem/algorithm should be divided into a smaller number of one or more modules. These smaller modules are further divided into one or more sub-modules . This process of decomposition is repeated until we achieve the desired output of module complexity. A top-down approach starts from the topmost module, and the modules are incremented accordingly until a level is reached where we don’t require any more sub-modules, that is, the desired level of complexity is achieved.

      FIGURE 1.5 Top-down approach

      Bottom-Up Approach–A bottom-up algorithm design approach is the opposite of a top-down approach. In this kind of approach, we first start with designing the basic modules and proceed further toward designing the high-level modules. The sub-modules are grouped to form a module of a higher level. Similarly, all high-level modules are grouped to form more high-level modules. Thus, this process of combining the sub-modules is repeated until we obtain the desired output of the algorithm.

      FIGURE 1.6 Bottom-up approach

      1.6 ANALYZING AN ALGORITHM

      An algorithm can be analyzed by two factors: space and time. We should develop an algorithm that makes the best use of both these resources. Analyzing an algorithm measures the efficiency of the algorithm. The efficiency of the algorithm is measured in terms of the speed and time complexity. The complexity of an algorithm is a function that measures the space and time used by an algorithm in terms of input size.

      Time Complexity–The time complexity of an algorithm is the amount of time taken by an algorithm to run the program completely. It is the runtime of the program. The time complexity of an algorithm depends upon the input size. The time complexity is commonly represented by using big O notation. For example, the time complexity of a linear search is O (n).

      Space Complexity–The space complexity of an algorithm is the amount of memory space required to run the program completely. The space complexity of an algorithm depends upon the input size.

      Time Complexity is categorized into three types:

      1. Best Case Running Time –The performance of the algorithm is best under optimal conditions. For example, the best case for a binary search occurs when the desired element is the middle element of the list. Another example is that of sorting; that is, if the elements are already sorted in a list, then the algorithm will execute in the best time.

      2. Average Case Running Time –This denotes the behavior of an algorithm when the input is randomly drawn from a given collection or distribution. It is an estimate of the running time for average input. It is usually assumed that all inputs of a given size are likely to occur with equal probability.

      3. Worst Case Running Time –The behavior of the algorithm during the worst possible case of the input instance. The worst case running time of an algorithm is an upper bound on the running time for any input. For example, the worst case for a linear search occurs when the desired element is the last element in the list or the element does not exist in the list.

      Frequently Asked Questions

      6. Define time complexity.

      Answer:

      Time complexity is a measure that evaluates the count of the operations performed by a given algorithm as a function of the size of the input. It is the approximation of the number of steps necessary to execute an algorithm. It is commonly represented with asymptotic notation, that is, the O(g) notation, also known as big O notation, where g is the function of the size of the input data.

      1.6.1 Time-Space Trade-Off

      In computer science, the time-space trade-off is a way of solving a particular problem either in less time and more memory space or in more time and less memory space. But if we talk in practical terms, designing such an algorithm in which we can save both space and time is a challenging task. So, we can use more than one algorithm to solve a problem. One may require less time, and the other may require less memory space to execute. Therefore, we sacrifice one thing for the other. Hence, there exists a time-space or time-memory trade-off between algorithms. The time-space trade-off gives the programmer a rational choice from an informed point of view. If time is a big concern for a programmer, then she might choose a program that takes less or the minimum time to execute. If space is a prime concern for a programmer, then, she might choose a program that takes less memory space to execute at the cost of more time.

      1.7 ABSTRACT DATA TYPES

      An Abstract Data Type (ADT) is a popular mathematical model of data objects that defines a data type along with various functions that operate on these objects. To understand the meaning of an abstract data type, we can break the term into two parts, that is, data type and abstract. The data type of a variable is a collection of values that a variable can take. There are various data types in Python (such as integer, float, character, long, and double). When we

      Enjoying the preview?
      Page 1 of 1