Books: 193

Python

CoverTitleYear
From Novice to Professional
Gain a fundamental understanding of Python’s syntax and features with this up–to–date introduction and practical reference. Covering a wide array of Python–related programming topics, including addressing language internals, database integration, network programming, and web services, you’ll be guided more » by sound development principles. Ten accompanying projects will ensure you can get your hands dirty in no time. Updated to reflect the latest in Python programming paradigms and several of the most crucial features found in Python 3, Beginning Python also covers advanced topics such as extending Python and packaging/distributing Python applications. What You'll Learn * Become a proficient Python programmer by following along with a friendly, practical guide to the language’s key features * Write code faster by learning how to take advantage of advanced features such as magic methods, exceptions, and abstraction * Gain insight into modern Python programming paradigms including testing, documentation, packaging, and distribution * Learn by following along with ten interesting projects, including a P2P file–sharing application, chat client, video game, remote text editor, and more Who This Book Is For Programmers, novice and otherwise, seeking a comprehensive introduction to the Python programming language. « less
2017
James McCaffrey’s SciPy Programming Succinctly offers readers a quick, thorough grounding in knowledge of the Python open source extension SciPy. The SciPy library, accompanied by its interdependent NumPy, offers Python programmers advanced functions that work with arrays and matrices. Each section presents more » a complete demo program for programmers to experiment with, carefully chosen examples to best illustrate each function, and resources for further learning. Use this e-book to install and edit SciPy, and use arrays, matrices, and combinatorics in Python programming. « less
2017
The Python ecosystem is vast and far-reaching in both scope and depth. Starting out in this crazy, open-source forest is daunting, and even with years of experience, it still requires continual effort to keep up-to-date with the best libraries and techniques. This report helps you explore some of the more » lesser known Python libraries and tools, including third-party modules and several extremely useful tools in the standard library that deserve more attention. What makes this collection different from other lists online? Author Caleb Hattingh diligently spent time finding and testing hidden gems that fit several criteria: easy to install and use, cross-platform, applicable to more than one domain, and not yet popular but likely to become so soon. You will likely discover at least a couple of cool libraries that will assist you in your everyday Python activities, no matter your specialization. This report examines: * Little-known standard library modules: collections, contextlib, concurrent.futures, logging, and sched * Flit for simplifying the process of submitting a Python package to the Python Package Index (PyPI) * Colorama and begins for making your command-line applications friendlier for users * Pyqtgraph and pywebview for creating graphical user interfaces (GUIs) * Watchdog, psutil, and ptpython for working closely with the operating system * Hug for exposing APIs for other users' programs to consume * Arrow and parsedatetime for working with dates and times * Third-party general-purpose libraries: Boltons, Cython, and the awesome-python curated list « less
2016
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 more » "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 « less
2016
KEY FEATURES * Simplify the Bayes process for solving complex statistical problems using Python; * Tutorial guide that will take the you through the journey of Bayesian analysis with the help of sample problems and practice exercises; * Learn how and when to use Bayesian analysis in your applications more » with this guide. BOOK DESCRIPTION The purpose of this book is to teach the main concepts of Bayesian data analysis. We will learn how to effectively use PyMC3, a Python library for probabilistic programming, to perform Bayesian parameter estimation, to check models and validate them. This book begins presenting the key concepts of the Bayesian framework and the main advantages of this approach from a practical point of view. Moving on, we will explore the power and flexibility of generalized linear models and how to adapt them to a wide array of problems, including regression and classification. We will also look into mixture models and clustering data, and we will finish with advanced topics like non-parametrics models and Gaussian processes. With the help of Python and PyMC3 you will learn to implement, check and expand Bayesian models to solve data analysis problems. WHAT YOU WILL LEARN * Understand the essentials Bayesian concepts from a practical point of view * Learn how to build probabilistic models using the Python library PyMC3 * Acquire the skills to sanity-check your models and modify them if necessary * Add structure to your models and get the advantages of hierarchical models * Find out how different models can be used to answer different data analysis questions * When in doubt, learn to choose between alternative models. * Predict continuous target outcomes using regression analysis or assign classes using logistic and softmax regression. * Learn how to think probabilistically and unleash the power and flexibility of the Bayesian framework ABOUT THE AUTHOR Osvaldo Martin is a researcher at The National Scientific and Technical Research Council (CONICET), the main organization in charge of the promotion of science and technology in Argentina. He has worked on structural bioinformatics and computational biology problems, especially on how to validate structural protein models. He has experience in using Markov Chain Monte Carlo methods to simulate molecules and loves to use Python to solve data analysis problems. He has taught courses about structural bioinformatics, Python programming, and, more recently, Bayesian data analysis. Python and Bayesian statistics have transformed the way he looks at science and thinks about problems in general. Osvaldo was really motivated to write this book to help others in developing probabilistic models with Python, regardless of their mathematical background. He is an active member of the PyMOL community (a C/Python-based molecular viewer), and recently he has been making small contributions to the probabilistic programming library PyMC3. TABLE OF CONTENTS 1. Thinking Probabilistically - A Bayesian Inference Primer 2. Programming Probabilistically – A PyMC3 Primer 3. Juggling with Multi-Parametric and Hierarchical Models 4. Understanding and Predicting Data with Linear Regression Models 5. Classifying Outcomes with Logistic Regression 6. Model Comparison 7. Mixture Models 8. Gaussian Processes « less
2016
Collect - Organize - Explore - Predict - Value
Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze more » data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python. Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data. This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data. Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume. Keep this handy quick guide at your side whether you're a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option. What You Need: You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io. If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com). Both packages are free and run on Windows, Linux, and Mac OS. « less
2016
*** Key Features *** * Based on latest stable version of Python(version 3.5) * Creating well manageable code that will run in various environments with different sets of dependencies * Packed with advanced concepts and best practices to write efficient Python code *** Book Description *** Python more » is a dynamic programming language, used in a wide range of domains by programmers who find it simple, yet powerful. Even if you find writing Python code easy, writing code that is efficient and easy to maintain and reuse is a challenge. The focus of the book is to familiarize you with common conventions, best practices, useful tools and standards used by python professionals on a daily basis when working with code. You will begin with knowing new features in Python 3.5 and quick tricks for improving productivity. Next, you will learn advanced and useful python syntax elements bought in this new version. Using advanced object-oriented concepts and mechanisms available in python, you will learn different approaches to implement metaprogramming. You will learn to choose good names, write packages and create standalone exectuables easily. You will also be using some powerful tools such as buildout and vitualenv to release and deploy the code on remote servers for production use. Moving on, you will learn to effectively create Python extensions with C, C++, cython and pyrex. Important factors while writing code such as code management tools, writing clear documentation and test driven development are also covered. You will now dive deeper to make your code efficient with general rules of optimization, strategies for finding bottlenecks and selected tools for application optimization By the end of the book you will be an expert in writing efficient and maintainable code. *** What you will learn *** * Conventions and best practices that are widely adopted in python community * Package python code effectively for community and production use * Easy and lightweight ways to automate code deployment on remote systems * Improve your code s quality, reliability and performance * Write concurrent code in python * Extend python with code written in different languages « less
2016
I’ve read many books but I never understood how time consuming it is to write one until I started working on chapter 12 of this book. That’s when a better arrangement occurred to me and I had to re-organize and ripple the changes through all the other chapters. With that in mind I would greatly appreciate more » an email if you encounter a section that causes confusion or if you encounter a typo or other mistake. « less
2016
Making Interactive Graphics with Python's Processing Mode
Processing opened up the world of programming to artists, designers, educators, and beginners. The Processing.py Python implementation of Processing reinterprets it for today's web. This short book gently introduces the core concepts of computer programming and working with Processing. Written by the more » co-founders of the Processing project, Reas and Fry, along with co-author Allison Parrish, Getting Started with Processing.py is your fast track to using Python's Processing mode. « less
2016
With Application to Understanding Data
This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some more » of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters. Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics. « less
2016