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| e in python math: Python in a Nutshell Alex Martelli, 2006-07-14 This volume offers Python programmers a straightforward guide to the important tools and modules of this open source language. It deals with the most frequently used parts of the standard library as well as the most popular and important third party extensions. |
| e in python math: Doing Math with Python Amit Saha, 2015-08-01 Doing Math with Python shows you how to use Python to delve into high school–level math topics like statistics, geometry, probability, and calculus. You’ll start with simple projects, like a factoring program and a quadratic-equation solver, and then create more complex projects once you’ve gotten the hang of things. Along the way, you’ll discover new ways to explore math and gain valuable programming skills that you’ll use throughout your study of math and computer science. Learn how to: –Describe your data with statistics, and visualize it with line graphs, bar charts, and scatter plots –Explore set theory and probability with programs for coin flips, dicing, and other games of chance –Solve algebra problems using Python’s symbolic math functions –Draw geometric shapes and explore fractals like the Barnsley fern, the Sierpinski triangle, and the Mandelbrot set –Write programs to find derivatives and integrate functions Creative coding challenges and applied examples help you see how you can put your new math and coding skills into practice. You’ll write an inequality solver, plot gravity’s effect on how far a bullet will travel, shuffle a deck of cards, estimate the area of a circle by throwing 100,000 darts at a board, explore the relationship between the Fibonacci sequence and the golden ratio, and more. Whether you’re interested in math but have yet to dip into programming or you’re a teacher looking to bring programming into the classroom, you’ll find that Python makes programming easy and practical. Let Python handle the grunt work while you focus on the math. Uses Python 3 |
| e in python math: Programming with Python T R Padmanabhan, 2017-01-13 Based on the latest version of the language, this book offers a self-contained, concise and coherent introduction to programming with Python. The book’s primary focus is on realistic case study applications of Python. Each practical example is accompanied by a brief explanation of the problem-terminology and concepts, followed by necessary program development in Python using its constructs, and simulated testing. Given the open and participatory nature of development, Python has a variety of incorporated data structures, which has made it difficult to present it in a coherent manner. Further, some advanced concepts (super, yield, generator, decorator, etc.) are not easy to explain. The book specially addresses these challenges; starting with a minimal subset of the core, it offers users a step-by-step guide to achieving proficiency. |
| e in python math: Programming in Python Pooja Sharma, 2020-04-09 An interactive way to introduce the world of Python Programming KEY FEATURES Detailed comparisons and differentiation of python language from other most popular languages C/C++/Java. Authentic and extensive set of programming illustrations in every chapter of the book. Broad study on all the programming constructs of the python programming language such as native data types, looping, decision making, exception handling, file handling etc. Broad study of Python Object Oriented Programming features with illustrations. Numerous review questions and exercises at the end of every chapter. DESCRIPTION This Book is meant for wide range of readers who wish to learn the basics of Python programming language. It can be helpful for students, programmers, researchers, and software developers. The basic concepts of python programming are dealt in detail. The various concepts of python language such as object-oriented features, operators, native data types, control structures, functions, exception handling, file handling, etc are discussed in detail with the authentic programming illustration of each. presently, python programming is a hot topic among academicianÕs researchers, and program developers. As a result, the book is designed to give an in-depth knowledge of programming in python. This book can be used as handbook as well as a guide for students of all computer science stream at any grade beginning from 10+1 to Research in PhD. To conclude, we hope that the readers will find this book a helpful guide and valuable source of information about python programming. WHAT WILL YOU LEARN Python Data Types, Input Output Operators and Expressions Control Structures Python Functions, Modules Exception Handling File Management, Classes and Objects Inheritance, Python Operator Overloading Ê WHO THIS BOOK IS FOR Students, Programmers, researchers, and software developers who wish to learn the basics of Python programming language. Ê Table of Contents 1. Introduction to Python Language 2. Python Data Types and Input Output 3. Operators and Expressions 4. Control Structures 5. Python Native Data Types 6. Python Functions 7. Python Modules 8. Exception Handling 9. File Management in Python 10. Classes and Objects 11. Inheritance 12. Python Operator Overloading |
| e in python math: Python All-in-One For Dummies John C. Shovic, Alan Simpson, 2019-04-18 Your one-stop resource on all things Python Thanks to its flexibility, Python has grown to become one of the most popular programming languages in the world. Developers use Python in app development, web development, data science, machine learning, and even in coding education classes. There's almost no type of project that Python can't make better. From creating apps to building complex websites to sorting big data, Python provides a way to get the work done. Python All-in-One For Dummies offers a starting point for those new to coding by explaining the basics of Python and demonstrating how it’s used in a variety of applications. Covers the basics of the language Explains its syntax through application in high-profile industries Shows how Python can be applied to projects in enterprise Delves into major undertakings including artificial intelligence, physical computing, machine learning, robotics and data analysis This book is perfect for anyone new to coding as well as experienced coders interested in adding Python to their toolbox. |
| e in python math: Python Basics Dan Bader, Joanna Jablonski, Fletcher Heisler, 2021-03-16 Make the Leap From Beginner to Intermediate in Python... Python Basics: A Practical Introduction to Python 3 Your Complete Python Curriculum-With Exercises, Interactive Quizzes, and Sample Projects What should you learn about Python in the beginning to get a strong foundation? With Python Basics, you'll not only cover the core concepts you really need to know, but you'll also learn them in the most efficient order with the help of practical exercises and interactive quizzes. You'll know enough to be dangerous with Python, fast! Who Should Read This Book If you're new to Python, you'll get a practical, step-by-step roadmap on developing your foundational skills. You'll be introduced to each concept and language feature in a logical order. Every step in this curriculum is explained and illustrated with short, clear code samples. Our goal with this book is to educate, not to impress or intimidate. If you're familiar with some basic programming concepts, you'll get a clear and well-tested introduction to Python. This is a practical introduction to Python that jumps right into the meat and potatoes without sacrificing substance. If you have prior experience with languages like VBA, PowerShell, R, Perl, C, C++, C#, Java, or Swift the numerous exercises within each chapter will fast-track your progress. If you're a seasoned developer, you'll get a Python 3 crash course that brings you up to speed with modern Python programming. Mix and match the chapters that interest you the most and use the interactive quizzes and review exercises to check your learning progress as you go along. If you're a self-starter completely new to coding, you'll get practical and motivating examples. You'll begin by installing Python and setting up a coding environment on your computer from scratch, and then continue from there. We'll get you coding right away so that you become competent and knowledgeable enough to solve real-world problems, fast. Develop a passion for programming by solving interesting problems with Python every day! If you're looking to break into a coding or data-science career, you'll pick up the practical foundations with this book. We won't just dump a boat load of theoretical information on you so you can sink or swim-instead you'll learn from hands-on, practical examples one step at a time. Each concept is broken down for you so you'll always know what you can do with it in practical terms. If you're interested in teaching others how to Python, this will be your guidebook. If you're looking to stoke the coding flame in your coworkers, kids, or relatives-use our material to teach them. All the sequencing has been done for you so you'll always know what to cover next and how to explain it. What Python Developers Say About The Book: Go forth and learn this amazing language using this great book. - Michael Kennedy, Talk Python The wording is casual, easy to understand, and makes the information flow well. - Thomas Wong, Pythonista I floundered for a long time trying to teach myself. I slogged through dozens of incomplete online tutorials. I snoozed through hours of boring screencasts. I gave up on countless crufty books from big-time publishers. And then I found Real Python. The easy-to-follow, step-by-step instructions break the big concepts down into bite-sized chunks written in plain English. The authors never forget their audience and are consistently thorough and detailed in their explanations. I'm up and running now, but I constantly refer to the material for guidance. - Jared Nielsen, Pythonista |
| e in python math: "Everything with Python" Nupur Soni, 2024-01-17 This book aims at providing fundamental concepts of Python programming. It is a good textbook basically designed for the CBSE curriculum for computer science. Here concepts are presented in the form of programs making it quite easy and simple for students to understand. It showcases actual screenshots of the programs from the programming environment to make it more student-friendly. Because of the user-friendly interface provided in the book a novice learner can also learn Python programming without any difficulty. As Python is open source, programs written in this book can execute on different operating systems like Windows, Linux, and Mac, etc. this ONE book covers all the topics that are present in the curriculum of 11th (CS, IP) and 12 (CS, IP). |
| e in python math: "Python Mastery: A Complete Guide to Programming Excellence" RAMANA, 2024-04-18 Here is a description for the book *Python Mastery: A Complete Guide to Programming Excellence*: Unlock your full potential as a programmer with *Python Mastery: A Complete Guide to Programming Excellence*. This comprehensive book is designed to guide you from the fundamentals of Python programming to advanced concepts and best practices. Through clear explanations and hands-on exercises, you'll gain a solid understanding of core topics such as data types, control structures, functions, and modules. Dive deeper into object-oriented programming, file handling, and libraries like NumPy and Pandas. Explore powerful techniques for debugging, testing, and optimizing your code. Whether you're a beginner or an experienced developer, this guide will help you achieve mastery in Python and elevate your programming skills to new heights. |
| e in python math: Computer Science with Python Reeta Sahoo, Gagan Sahoo, A series of Book of Computers . The ebook version does not contain CD. |
| e in python math: The Python Standard Library by Example Doug Hellmann, 2011-06-01 “Hellmann’s writing has become an indispensable resource for me and many others as it fills a critical gap in Python Documentation with examples.” — Jesse Noller, Python Core Developer and PSF Board Member Master the Powerful Python Standard Library through Real Code Examples The Python Standard Library contains hundreds of modules for interacting with the operating system, interpreter, and Internet—all extensively tested and ready to jump-start your application development. The Python Standard Library by Example introduces virtually every important area of the Python 2.7 library through concise, stand-alone source code/output examples, designed for easy learning and reuse. Building on his popular Python Module of the Week blog series, author and Python expert Doug Hellmann focuses on “showing” not “telling.” He explains code behavior through downloadable examples that fully demonstrate each feature. You’ll find practical code for working with text, data types, algorithms, math, file systems, networking, the Internet, XML, email, cryptography, concurrency, runtime and language services, and much more. Each section fully covers one module, and links to valuable additional resources, making this book an ideal tutorial and reference. Coverage includes Manipulating text with string, textwrap, re, and difflib Implementing data structures: collections, array, queue, struct, copy, and more Reading, writing, and manipulating files and directories Regular expression pattern matching Exchanging data and providing for persistence Archiving and data compression Managing processes and threads Using application “building blocks”: parsing command-line options, prompting for passwords, scheduling events, and logging Testing, debugging, and compilation Controlling runtime configuration Using module and package utilities If you’re new to Python, this book will quickly give you access to a whole new world of functionality. If you’ve worked with Python before, you’ll discover new, powerful solutions and better ways to use the modules you’ve already tried. |
| e in python math: Python Essential Reference David M. Beazley, 2009 Python Essential Reference is the definitive reference guide to the Python programming language--the one authoritative handbook that reliably untangles and explains both the core Python library. Designed for the practicing programmer, the book is concise, to the point, and highly accessible. It also includes detailed information on the Python library and many advanced subjects that is not available in either the official Python documentation or any other single reference source. Thoroughly updated to reflect the significant new programming language features and library modules that have been introduced in Python 2.6 and Python 3, the fourth edition of Python Essential Reference is the complete guide for programmers who need to modernize existing Python code or who are planning an eventual migration to Python 3. |
| e in python math: A Primer on Scientific Programming with Python Hans Petter Langtangen, 2016-07-28 The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology and finance. The book teaches Matlab-style and procedural programming as well as object-oriented programming. High school mathematics is a required background and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science. From the reviews: Langtangen ... does an excellent job of introducing programming as a set of skills in problem solving. He guides the reader into thinking properly about producing program logic and data structures for modeling real-world problems using objects and functions and embracing the object-oriented paradigm. ... Summing Up: Highly recommended. F. H. Wild III, Choice, Vol. 47 (8), April 2010 Those of us who have learned scientific programming in Python ‘on the streets’ could be a little jealous of students who have the opportunity to take a course out of Langtangen’s Primer.” John D. Cook, The Mathematical Association of America, September 2011 This book goes through Python in particular, and programming in general, via tasks that scientists will likely perform. It contains valuable information for students new to scientific computing and would be the perfect bridge between an introduction to programming and an advanced course on numerical methods or computational science. Alex Small, IEEE, CiSE Vol. 14 (2), March /April 2012 “This fourth edition is a wonderful, inclusive textbook that covers pretty much everything one needs to know to go from zero to fairly sophisticated scientific programming in Python...” Joan Horvath, Computing Reviews, March 2015 |
| e in python math: Problem Solving with Python 3. 7 Edition Peter D. Kazarinoff, 2019-09-15 Get started solving problems with the Python programming language!This book introduces some of the most famous scientific libraries for Python: * Python's math and statistics module to do calculations * Matplotlib to build 2D and 3D plots * NumPy to complete calculations on arrays * Jupiter Notebooks to share results with a team * SymPy to solve equations * PySerial to control an Arduino with Python * MicroPython to control an LEDThis book is great for budding engineers and data scientists. The text starts with the basics but finishes with topics rarely included in other engineering and data science programming books like SymPy and PySerial and MicroPython. |
| e in python math: Essential Math for Data Science Thomas Nield, 2022-05-26 Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market |
| e in python math: Cracking Codes with Python Al Sweigart, 2018-01-23 Learn how to program in Python while making and breaking ciphers—algorithms used to create and send secret messages! After a crash course in Python programming basics, you’ll learn to make, test, and hack programs that encrypt text with classical ciphers like the transposition cipher and Vigenère cipher. You’ll begin with simple programs for the reverse and Caesar ciphers and then work your way up to public key cryptography, the type of encryption used to secure today’s online transactions, including digital signatures, email, and Bitcoin. Each program includes the full code and a line-by-line explanation of how things work. By the end of the book, you’ll have learned how to code in Python and you’ll have the clever programs to prove it! You’ll also learn how to: - Combine loops, variables, and flow control statements into real working programs - Use dictionary files to instantly detect whether decrypted messages are valid English or gibberish - Create test programs to make sure that your code encrypts and decrypts correctly - Code (and hack!) a working example of the affine cipher, which uses modular arithmetic to encrypt a message - Break ciphers with techniques such as brute-force and frequency analysis There’s no better way to learn to code than to play with real programs. Cracking Codes with Python makes the learning fun! |
| e in python math: Python Programming for Biology Tim J. Stevens, Wayne Boucher, 2015-02-12 Do you have a biological question that could be readily answered by computational techniques, but little experience in programming? Do you want to learn more about the core techniques used in computational biology and bioinformatics? Written in an accessible style, this guide provides a foundation for both newcomers to computer programming and those interested in learning more about computational biology. The chapters guide the reader through: a complete beginners' course to programming in Python, with an introduction to computing jargon; descriptions of core bioinformatics methods with working Python examples; scientific computing techniques, including image analysis, statistics and machine learning. This book also functions as a language reference written in straightforward English, covering the most common Python language elements and a glossary of computing and biological terms. This title will teach undergraduates, postgraduates and professionals working in the life sciences how to program with Python, a powerful, flexible and easy-to-use language. |
| e in python math: The Python 3 Standard Library by Example Doug Hellmann, 2017-06-14 This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Master the Powerful Python 3 Standard Library through Real Code Examples “The genius of Doug’s approach is that with 15 minutes per week, any motivated programmer can learn the Python Standard Library. Doug’s guided tour will help you flip the switch to fully power-up Python’s batteries.” –Raymond Hettinger, Distinguished Python Core Developer The Python 3 Standard Library contains hundreds of modules for interacting with the operating system, interpreter, and Internet–all extensively tested and ready to jump-start application development. Now, Python expert Doug Hellmann introduces every major area of the Python 3.x library through concise source code and output examples. Hellmann’s examples fully demonstrate each feature and are designed for easy learning and reuse. You’ll find practical code for working with text, data structures, algorithms, dates/times, math, the file system, persistence, data exchange, compression, archiving, crypto, processes/threads, networking, Internet capabilities, email, developer and language tools, the runtime, packages, and more. Each section fully covers one module, with links to additional resources, making this book an ideal tutorial and reference. The Python 3 Standard Library by Example introduces Python 3.x’s new libraries, significant functionality changes, and new layout and naming conventions. Hellmann also provides expert porting guidance for moving code from 2.x Python standard library modules to their Python 3.x equivalents. Manipulate text with string, textwrap, re (regular expressions), and difflib Use data structures: enum, collections, array, heapq, queue, struct, copy, and more Implement algorithms elegantly and concisely with functools, itertools, and contextlib Handle dates/times and advanced mathematical tasks Archive and data compression Understand data exchange and persistence, including json, dbm, and sqlite Sign and verify messages cryptographically Manage concurrent operations with processes and threads Test, debug, compile, profile, language, import, and package tools Control interaction at runtime with interpreters or the environment |
| e in python math: Programming With Python Vaibhav Gondaliya, 2019-08-30 About Book Are you looking to master the Python programming language? Look no further than 'Programming with Python'! This comprehensive guide covers everything from installing the Python software on your computer to advanced data file handling techniques. With step-by-step instructions and helpful visual aids, this book is the perfect resource for beginners and experienced programmers alike. Along with detailed explanations of every topic, the book also includes numerous solved programs and unsolved exercises to help reinforce your understanding. Whether you're starting from scratch or looking to expand your skills in database handling, 'Programming with Python' is the ultimate resource for anyone looking to become a Python expert. |
| e in python math: Python Programming and Numerical Methods Qingkai Kong, Timmy Siauw, Alexandre Bayen, 2020-11-27 Python Programming and Numerical Methods: A Guide for Engineers and Scientists introduces programming tools and numerical methods to engineering and science students, with the goal of helping the students to develop good computational problem-solving techniques through the use of numerical methods and the Python programming language. Part One introduces fundamental programming concepts, using simple examples to put new concepts quickly into practice. Part Two covers the fundamentals of algorithms and numerical analysis at a level that allows students to quickly apply results in practical settings. - Includes tips, warnings and try this features within each chapter to help the reader develop good programming practice - Summaries at the end of each chapter allow for quick access to important information - Includes code in Jupyter notebook format that can be directly run online |
| e in python math: Mathematical Logic through Python Yannai A. Gonczarowski, Noam Nisan, 2022-07-31 Using a unique pedagogical approach, this text introduces mathematical logic by guiding students in implementing the underlying logical concepts and mathematical proofs via Python programming. This approach, tailored to the unique intuitions and strengths of the ever-growing population of programming-savvy students, brings mathematical logic into the comfort zone of these students and provides clarity that can only be achieved by a deep hands-on understanding and the satisfaction of having created working code. While the approach is unique, the text follows the same set of topics typically covered in a one-semester undergraduate course, including propositional logic and first-order predicate logic, culminating in a proof of Gödel's completeness theorem. A sneak peek to Gödel's incompleteness theorem is also provided. The textbook is accompanied by an extensive collection of programming tasks, code skeletons, and unit tests. Familiarity with proofs and basic proficiency in Python is assumed. |
| e in python math: Python Programming C. K. Dhaliwal, Poonam Rana, T. P. S. Brar, 2024-08-02 This book explores Python's rich history, dynamic features, and its wide-ranging applications in web development, data science, and machine learning. From its installation process to interactive help, readers embark on a journey through Python's unique characteristics and its distinctions from other programming languages. It lays a solid foundation for beginners and seasoned programmers alike. Print edition not for sale in South Asia (India, Sri Lanka, Nepal, Bangladesh, Pakistan or Bhutan) |
| e in python math: Statistical Learning with Math and Python Joe Suzuki, 2021-08-03 The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning. Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercises in each chapter, readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning. |
| e in python math: Introduction to Programming in Python Robert Sedgewick, Kevin Wayne, Robert Dondero, 2015-05-27 Today, anyone in a scientific or technical discipline needs programming skills. Python is an ideal first programming language, and Introduction to Programming in Python is the best guide to learning it. Princeton University’s Robert Sedgewick, Kevin Wayne, and Robert Dondero have crafted an accessible, interdisciplinary introduction to programming in Python that emphasizes important and engaging applications, not toy problems. The authors supply the tools needed for students to learn that programming is a natural, satisfying, and creative experience. This example-driven guide focuses on Python’s most useful features and brings programming to life for every student in the sciences, engineering, and computer science. Coverage includes Basic elements of programming: variables, assignment statements, built-in data types, conditionals, loops, arrays, and I/O, including graphics and sound Functions, modules, and libraries: organizing programs into components that can be independently debugged, maintained, and reused Object-oriented programming and data abstraction: objects, modularity, encapsulation, and more Algorithms and data structures: sort/search algorithms, stacks, queues, and symbol tables Examples from applied math, physics, chemistry, biology, and computer science—all compatible with Python 2 and 3 Drawing on their extensive classroom experience, the authors provide Q&As, exercises, and opportunities for creative practice throughout. An extensive amount of supplementary information is available at introcs.cs.princeton.edu/python. With source code, I/O libraries, solutions to selected exercises, and much more, this companion website empowers people to use their own computers to teach and learn the material. |
| e in python math: Introduction to Engineering and Scientific Computing with Python David E. Clough, Steven C. Chapra, 2022-09-07 1) Provides a levelling approach, bringing students at all stages of programming experience to the same point 2) Focuses Python, a general language, to an engineering and scientific context 3) Uses a classroom tested, practical approach to teaching programming 4) Teaches students and professionals how to use Python to solve engineering calculations such as differential and algebraic equations |
| e in python math: Computational Modeling and Visualization of Physical Systems with Python Jay Wang, 2015-12-21 Computational Modeling, by Jay Wang introduces computational modeling and visualization of physical systems that are commonly found in physics and related areas. The authors begin with a framework that integrates model building, algorithm development, and data visualization for problem solving via scientific computing. Through carefully selected problems, methods, and projects, the reader is guided to learning and discovery by actively doing rather than just knowing physics. |
| e in python math: Python Chris Fehily, 2002 Named after the Monty Python comedy troupe, Python is an interpreted, open-source, object-oriented programming language. It's also free and runs portably on Windows, Mac OS, Unix, and other operating systems. Python can be used for all manner of programming tasks, from CGI scripts to full-fledged applications. It is gaining popularity among programmers in part because it is easier to read (and hence, debug) than most other programming languages, and it's generally simpler to install, learn, and use. Its line structure forces consistent indentation. Its syntax and semantics make it suitable for simple scripts and large programs. Its flexible data structures and dynamic typing allow you to get a lot done in a few lines. To learn it, you'll need is some basic programming experience and a copy of Python: Visual QuickStart Guide. In patented Visual QuickStart Guide fashion, the book doesn't just tell you how to use Python to develop applications, it shows you, breaking Python into easy-to-digest, step-by-step tasks and providing example code. Python: Visual QuickStart Guide emphasizes the core language and libraries, which are the building blocks for programs. Author Chris Fehily starts with the basics - expressions, statements, numbers, strings - then moves on to lists, dictionaries, functions, and modules before wrapping things up with straightforward discussions of exceptions and classes. Some additional topics covered include: - Object-oriented programming- Working in multiple operating systems- Structuring large programs- Comparing Python to C, Perl, and Java- Handling errors gracefully. |
| e in python math: Foundations of Applied Mathematics, Volume I Jeffrey Humpherys, Tyler J. Jarvis, Emily J. Evans, 2017-07-07 This book provides the essential foundations of both linear and nonlinear analysis necessary for understanding and working in twenty-first century applied and computational mathematics. In addition to the standard topics, this text includes several key concepts of modern applied mathematical analysis that should be, but are not typically, included in advanced undergraduate and beginning graduate mathematics curricula. This material is the introductory foundation upon which algorithm analysis, optimization, probability, statistics, differential equations, machine learning, and control theory are built. When used in concert with the free supplemental lab materials, this text teaches students both the theory and the computational practice of modern mathematical analysis. Foundations of Applied Mathematics, Volume 1: Mathematical Analysis includes several key topics not usually treated in courses at this level, such as uniform contraction mappings, the continuous linear extension theorem, Daniell?Lebesgue integration, resolvents, spectral resolution theory, and pseudospectra. Ideas are developed in a mathematically rigorous way and students are provided with powerful tools and beautiful ideas that yield a number of nice proofs, all of which contribute to a deep understanding of advanced analysis and linear algebra. Carefully thought out exercises and examples are built on each other to reinforce and retain concepts and ideas and to achieve greater depth. Associated lab materials are available that expose students to applications and numerical computation and reinforce the theoretical ideas taught in the text. The text and labs combine to make students technically proficient and to answer the age-old question, When am I going to use this? |
| e in python math: Euler's Pioneering Equation Robin Wilson, 2018-02-22 In 1988 The Mathematical Intelligencer, a quarterly mathematics journal, carried out a poll to find the most beautiful theorem in mathematics. Twenty-four theorems were listed and readers were invited to award each a 'score for beauty'. While there were many worthy competitors, the winner was 'Euler's equation'. In 2004 Physics World carried out a similar poll of 'greatest equations', and found that among physicists Euler's mathematical result came second only to Maxwell's equations. The Stanford mathematician Keith Devlin reflected the feelings of many in describing it as like a Shakespearian sonnet that captures the very essence of love, or a painting which brings out the beauty of the human form that is far more than just skin deep, Euler's equation reaches down into the very depths of existence. What is it that makes Euler's identity, eiπ + 1 = 0, so special? In Euler's Pioneering Equation Robin Wilson shows how this simple, elegant, and profound formula links together perhaps the five most important numbers in mathematics, each associated with a story in themselves: the number 1, the basis of our counting system; the concept of zero, which was a major development in mathematics, and opened up the idea of negative numbers; π an irrational number, the basis for the measurement of circles; the exponential e, associated with exponential growth and logarithms; and the imaginary number i, the square root of -1, the basis of complex numbers. Following a chapter on each of the elements, Robin Wilson discusses how the startling relationship between them was established, including the several near misses to the discovery of the formula. |
| e in python math: PYTHON ESSENTIALS Mr. Ajay Gupta, Dr. Prabhat Kumar Srivastava, Ms. Mamta Srivastava, Mrs. Priya Gupta, The book titled Python Essentials' covers complete syllabus of Concept of Python Programming prescribed by Technical University of Uttar Pradesh and other Universities also. This book builds on the concepts of Python programming language introduced in Several Class. The book is replete with a rich pedagogy comprising true-or-false, multiple-choice apart from programming problems of varying difficulty levels to help students ace their exams with ease. Amply supported by illustrative diagrams, keywords and topic highlights, this book is an ideal text that helps students build a firm foundation in the subject The book titled Python Essentials' covers complete syllabus of Concept of Python Programming prescribed by Technical University of Uttar Pradesh and other Universities also. This book builds on the concepts of Python programming language introduced in Class XI. The book is replete with a rich pedagogy comprising true-or-false, multiple-choice apart from programming problems of varying difficulty levels to help students ace their exams with ease. Amply supported by illustrative diagrams, keywords and topic highlights, this book is an ideal text that helps students build a firm foundation in the subject. |
| e in python math: WRITE YOUR FIRST PROGRAM AMIT SAHA, 2013-04-25 This introductory book on programming introduces computer programming using C and Python programming languages on Microsoft Windows and Linux operating systems to beginners. The book assumes no familiarity with programming and teaches the basics of programming to its readers. It helps the readers to write programs to solve problems in computer science, finance, mathematics and physics. Unlike other introductory guides to programming, Write Your First Program focuses on the exact information that beginners are required to apply while creating practical programs. The book is organized in eight chapters–with each chapter introducing a major programming topic, focusing on the concepts and then implementing them in both the languages. This book will teach you to write your first program and progress on to concepts such as working with data, decision making, persistent data storage and implementing mathematical operations. Apart from programming, the book also discusses version control systems and open source projects. The aim of the book is to focus on the programming logic, and then see how the logic can be implemented using two different languages. Thus, it helps the readers to learn two vastly different ways of programming. This book is intended for all those who are interested to learn/sharpen their programming skills. Companion Website The website for this book (www.phindia.com/saha) is an integral part of the book where you will find: • Extended treatment of certain topics • Additional tips and tutorials • Questions and comments page |
| e in python math: A Simple Introduction to Python Stephen Lynch, 2024-06-11 A Simple Introduction to Python is aimed at pre-university students and complete novices to programming. The whole book has been created using Jupyter notebooks. After introducing Python as a powerful calculator, simple programming constructs are covered, and the NumPy, MatPlotLib and SymPy modules (libraries) are introduced. Python is then used for Mathematics, Cryptography, Artificial Intelligence, Data Science and Object Oriented Programming. The reader is shown how to program using the integrated development environments: Python IDLE, Spyder, Jupyter notebooks, and through cloud computing with Google Colab. Features: No prior experience in programming is required. Demonstrates how to format Jupyter notebooks for publication on the Web. Full solutions to exercises are available as a Jupyter notebook on the Web. All Jupyter notebook solution files can be downloaded through GitHub. GitHub Repository of Data Files and a Jupyter Solution notebook: https://github.com/proflynch/A-Simple-Introduction-to-Python Jupyter Solution notebook web page: https://drstephenlynch.github.io/webpages/A-Simple-Introduction-to-Python-Solutions.html |
| e in python math: Recent Advances in Modeling and Forecasting Kaiyu Saburo Saito, |
| e in python math: Programming for Computations - Python Svein Linge, Hans Petter Langtangen, 2016-07-25 This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification. |
| e in python math: A Computational Introduction to Digital Image Processing Alasdair McAndrew, 2015-10-28 Highly Regarded, Accessible Approach to Image Processing Using Open-Source and Commercial SoftwareA Computational Introduction to Digital Image Processing, Second Edition explores the nature and use of digital images and shows how they can be obtained, stored, and displayed. Taking a strictly elementary perspective, the book only covers topics that |
| e in python math: Python for Data Science Fundamentals Dr.S.Peerbasha, Mr.A.Basheer Ahamed, Mr.P.Shivaathmajan, Dr.Pavithra.M, Dr.T.Suresh, 2024-07-21 Dr.S.Peerbasha, Assistant Professor, Department of Computer Science, Jamal Mohamed College, Tiruchirappalli, Tamil Nadu, India. Mr.A.Basheer Ahamed, Assistant Professor, Department of Computer Science, Jamal Mohamed College, Tiruchirappalli, Tamil Nadu, India. Mr.P.Shivaathmajan, Student, B.Tech IT, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India. Dr.Pavithra.M, Assistant Professor, Department of Computer Science and Engineering, Jansons Institute of Technology, Karumathampatti, Coimbatore, Tamil Nadu, India. Dr.T.Suresh, Assistant Professor, Department of Artificial Intelligence Machine Learning, K.Ramakrishnan College of Engineering, Tiruchirappalli, Tamil Nadu, India. |
| e in python math: Oswaal CBSE Question Bank Class 11 Computer Science, Chapterwise and Topicwise Solved Papers For 2025 Exams Oswaal Editorial Board, 2024-02-03 Description of the product: • 100% Updated Syllabus & Question Typologies: We have got you covered with the latest and 100% updated curriculum along with the latest typologies of Questions. • Timed Revision with Topic-wise Revision Notes & Smart Mind Maps: Study smart, not hard! • Extensive Practice with 1000+ Questions & SAS Questions (Sri Aurobindo Society): To give you 1000+ chances to become a champ! • Concept Clarity with 500+ Concepts & Concept Videos: For you to learn the cool way— with videos and mind-blowing concepts. • NEP 2020 Compliance with Competency-Based Questions & Artificial Intelligence: For you to be on the cutting edge of the coolest educational trends. |
| e in python math: Python in High School Arnaud Bodin, 2020-03-10 Python is the ideal language to learn programming. It is a powerful language that will immerse you in the world of algorithms. This book guides you step by step through original mathematical and computer activities adapted to high school. It is complemented by online resources: all the Python codes and colourful chapters. You have everything you need to succeed!* Hello world! * Turtle (Scratch with Python) * If ... then ... * Functions * Arithmetic - While loop - I * Strings - Analysis of a text * Lists I * Statistics - Data visualization * Files * Arithmetic - While loop - II * Binary I * Lists II * Binary II * Probabilities - Parrondo's paradox * Find and replace * Polish calculator - Stacks * Text viewer -Markdown * L-systems * Dynamic images * Game of life * Ramsey graphs and combinatorics * Bitcoin * Random blocks * |
| e in python math: Operations Research Himanshu, 2018-11-10 Operations research encompasses a wide range of problem-solving techniques and methods applied in the pursuit of improved decision-making and efficiency. Some of the tools used by operations researchers are statistics, optimization, probability theory, queuing theory, game theory, graph theory, decision analysis, mathematical modeling and simulation. An Information System is any combination of information technology and people's activities using that technology to support operations, management, and decision-making. In a very broad sense, the term information system is frequently used to refer to the interaction between people, algorithmic processes, data and technology. Operations Research is the scientific study of logistic networks to provide for decision support at all levels in order to optimize production and distribution of the commodity flows. Nowadays, these logistic networks have become very large and may range over several countries, while the demands for quality of service have grown similarly to ever higher standards. Generally one agrees that to maintain such large networks successfully, one needs the control of all the information flows through the network, that is, continuous information on the status of the resources. Operations research is an interdisciplinary branch of applied mathematics and formal science that uses advanced analytical methods such as mathematical modeling, statistical analysis, and mathematical optimization to arrive at optimal or near-optimal solutions to complex decision-making problems. It is often concerned with determining the maximum or minimum of some real-world objective. The book of operations management features the latest concepts and applications while not losing focus on the core concepts that has made this text a market leader. |
| e in python math: Machine Learning for Neuroscience Chuck Easttom, 2023-07-31 This book addresses the growing need for machine learning and data mining in neuroscience. The book offers a basic overview of the neuroscience, machine learning and the required math and programming necessary to develop reliable working models. The material is presented in a easy to follow user-friendly manner and is replete with fully working machine learning code. Machine Learning for Neuroscience: A Systematic Approach, tackles the needs of neuroscience researchers and practitioners that have very little training relevant to machine learning. The first section of the book provides an overview of necessary topics in order to delve into machine learning, including basic linear algebra and Python programming. The second section provides an overview of neuroscience and is directed to the computer science oriented readers. The section covers neuroanatomy and physiology, cellular neuroscience, neurological disorders and computational neuroscience. The third section of the book then delves into how to apply machine learning and data mining to neuroscience and provides coverage of artificial neural networks (ANN), clustering, and anomaly detection. The book contains fully working code examples with downloadable working code. It also contains lab assignments and quizzes, making it appropriate for use as a textbook. The primary audience is neuroscience researchers who need to delve into machine learning, programmers assigned neuroscience related machine learning projects and students studying methods in computational neuroscience. |
| e in python math: Inventory Analytics Roberto Rossi, 2021-05-24 Inventory Analytics provides a comprehensive and accessible introduction to the theory and practice of inventory control – a significant research area central to supply chain planning. The book outlines the foundations of inventory systems and surveys prescriptive analytics models for deterministic inventory control. It further discusses predictive analytics techniques for demand forecasting in inventory control and also examines prescriptive analytics models for stochastic inventory control. Inventory Analytics is the first book of its kind to adopt a practicable, Python-driven approach to illustrating theories and concepts via computational examples, with each model covered in the book accompanied by its Python code. Originating as a collection of self-contained lectures, Inventory Analytics will be an indispensable resource for practitioners, researchers, teachers, and students alike. |
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