This book assumes that everyone needs to know how to program, and that once you know how to program you will figure out what you want to do with your newfound skills. Download free ebook intituled Python for Everybody, creative commons document
This book uses Python to introduce folks to programming and algorithmic thinking. It is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques.
Submitted On : 2020-08-27
Taille : PDF Files and a single PDF (360 pages, 2.1 MB)
This book is assembled from lectures given by the author over a period of 10 years at the School of Computing of DePaul University. The lectures cover multiple classes, including Analysis and Design of Algorithms, Scientific Computing, Monte Carlo Simulations, and Parallel Algorithms. These lectures teach the core knowledge required by any scientist interested in numerical algorithms and by students interested in computational finance.
A Whirlwind Tour of Python is a fast-paced introduction to essential features of the Python language, aimed at researchers and developers who are already familiar with programming in another language. The material is particularly designed for those who wish to use Python for data science and/or scientific programming, and in this capacity serves as an introduction to my longer book, The Python Data Science Handbook.
This book is a complete presentation of the Python language. It is oriented toward learning, which involves accumulating many closely intertwined concepts. In our experience teaching, coaching and doing programming, there is an upper limit on the "clue absorption rate". In order to keep within this limit, we've found that it helps to present a language as ever-expanding layers. We'll lead you from a very tiny, easy to understand subset of statements to the entire Python language and all of the built-in data structures. We've also found that doing a number of exercises helps internalize each language concept.
This book deepens your knowledge of problem-solving techniques from the realm of computer science by challenging you with time-tested scenarios, exercises, and algorithms. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your "new" problems!
This book written to provide clear and concise explanation of topics for programmers both starting to learn the Python programming language as well as those diving in more complex topics. Most examples are linked to online playground that allows you to change the code and re-run it.
This book aims to enable the reader to quickly acquire a Python foundation. The material particularly feel quite comfortable to anyone with background in an object-oriented programming (OOP) language such as C++ or Java.
Python is a widely used general-purpose, high-level programming language. Its design philosophy emphasizes code readability, and its syntax allows programmers to express concepts in fewer lines of code than would be possible in languages such as C. The language provides constructs intended to enable clear programs on both a small and large scale.
An introduction to computer programming with Python 3. Helps the readers in learning the key concepts of Python and understanding how programs work while also imparting foundational logic that can serve the readers in other domains.
This book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming.
This book contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning.
This book is a tutorial for the Python 3 programming language designed for someone with no programming experience. Starting from no programming knowledge, the book teaches how to create programs with examples, explanations and exercises.
This books is about computer science. It is also about Python. However, there is much more. The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter. The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understanding before continuing on to the more complex parts of the curriculum. In addition, a beginner needs to be given the opportunity to be successful and gain confidence.
This second edition of the well-received book has been extensively revised: All code is now written in Python version 3.6 (no longer version 2.7). In addition, the two first chapters of the previous edition have been extended and split up into five new chapters, thus expanding the introduction to programming from 50 to 150 pages
This book is aimed at more experienced Python programmers who are looking to deepen their understanding of the language and modern programming idioms. Much of the material focuses on some of the more advanced techniques used by libraries, frameworks, and applications.
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all - IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.
Python for Everybody is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.
This book goes beyond the basics to teach beginner- and intermediate-level Python programmers the little-known tools and constructs that build concise, maintainable code. Design better architecture and write easy-to-understand code using highly adoptable techniques that result in more robust and efficient applications.
This book consists of a set of is tutorials on the scientific Python ecosystem: a quick introduction to central tools and techniques. The different chapters each correspond to a 1 to 2 hours course with increasing level of expertise, from beginner to expert.
This book offers a concise and gentle introduction to finite element programming in Python based on the popular FEniCS software library. Using a series of examples, including the Poisson equation, the equations of linear elasticity, the incompressible Navier–Stokes equations, and systems of nonlinear advection–diffusion–reaction equations, it guides readers through the essential steps to quickly solving a PDE in FEniCS, such as how to define a finite variational problem, how to set boundary conditions, how to solve linear and nonlinear systems, and how to visualize solutions and structure finite element Python programs.
By taking you through the development of a real web application from beginning to end, this hands-on guide demonstrates the practical advantages of test-driven development (TDD) with Python. You'll learn how to write and run tests before building each part of your app, and then develop the minimum amount of code required to pass those tests. The result? Clean code that works.
If you know how to program with Python and also know a little about probability, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you'll begin to apply these techniques to real-world problems.
This book is an introduction to signal processing and system analysis using a computational approach, using Python as the programming language. The premise of this book (like the others in the Think X series) is that if you know how to program, you can use that skill to learn other things. By the end of the first chapter, you'll be able to decompose a sound into its harmonics, modify the harmonics, and generate new sounds. Subsequent chapters follow a logical progression that develops the important ideas incrementally, with a focus on applications.
Python for Software Design is a concise introduction to software design using the Python programming language. Intended for people with no programming experience, this book starts with the most basic concepts and gradually adds new material. Some of the ideas students find most challenging, like recursion and object-oriented programming, are divided into a sequence of smaller steps and introduced over the course of several chapters. The focus is on the programming process, with special emphasis on debugging.
Think Python is an introduction to Python programming for students with no programming experience. It starts with the most basic concepts of programming, and is carefully designed to define all terms when they are first used and to develop each new concept in a logical progression. Larger pieces, like recursion and object-oriented programming are divided into a sequence of smaller steps and introduced over the course of several chapters.
THIS TEXTBOOK is about computer science. It is also about Python. However, there is much more. The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter. The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understanding before continuing on to the more complex parts of the curriculum. In addition, a beginner needs to be given the opportunity to be successful and gain confidence.
Download free course Biopython: Tutorial and Cookbook, pdf file on 360 pages by by Jeff Chang, Brad Chapman, Iddo Friedberg, Thomas Hamelryck, Michiel de Hoon, Peter Cock, Tiago Antao, Eric Talevich, Bartek Wilczy?ski.