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Python for MATLAB Development: Extend MATLAB with 300,000+ Modules from the Python Package Index
by Albert DanialMATLAB can run Python code!Python for MATLAB Development shows you how to enhance MATLAB with Python solutions to a vast array of computational problems in science, engineering, optimization, statistics, finance, and simulation. It is three books in one:A thorough Python tutorial that leverages your existing MATLAB knowledge with a comprehensive collection of MATLAB/Python equivalent expressionsA reference guide to setting up and managing a Python environment that integrates cleanly with MATLABA collection of recipes that demonstrate Python solutions invoked directly from MATLAB This book shows how to call Python functions to enhance MATLAB's capabilities. Specifically, you'll see how Python helps MATLAB:Run faster with numbaDistribute work to a compute cluster with daskFind symbolic solutions to integrals, derivatives, and series summations with SymPyOverlay data on maps with CartopySolve mixed-integer linear programming problems with PuLPInteract with Redis via pyredis, PostgreSQL via psycopg2, and MongoDB via pymongoRead and write file formats that are not natively understood by MATLAB, such as SQLite, YAML, and iniWho This Book Is ForMATLAB developers who are new to Python and other developers with some prior experience with MATLAB, R, IDL, or Mathematica.
Python for MBAs
by Mattan Griffel Daniel GuettaFrom the ads that track us to the maps that guide us, the twenty-first century runs on code. The business world is no different. Programming has become one of the fastest-growing topics at business schools around the world. An increasing number of MBAs are choosing to pursue careers in tech. For them and other professionals, having some basic coding knowledge is a must.This book is an introduction to programming with Python for MBA students and others in business positions who need a crash course. One of the most popular programming languages, Python is used for tasks such as building and running websites, data analysis, machine learning, and natural-language processing. Drawing on years of experience providing instruction in this material at Columbia Business School as well as extensive backgrounds in technology, entrepreneurship, and consulting, Mattan Griffel and Daniel Guetta teach the basics of programming from scratch. Beginning with fundamentals such as variables, strings, lists, and functions, they build up to data analytics and practical ways to derive value from large and complex datasets. They focus on business use cases throughout, using the real-world example of a major restaurant chain to offer a concrete look at what Python can do. Written for business students with no previous coding experience and those in business roles that include coding or working with coding teams, Python for MBAs is an indispensable introduction to a versatile and powerful programming language.
Python for Marketing Research and Analytics
by Chris Chapman Elea McDonnell Feit Jason S. SchwarzThis book provides an introduction to quantitative marketing with Python. The book presents a hands-on approach to using Python for real marketing questions, organized by key topic areas. Following the Python scientific computing movement toward reproducible research, the book presents all analyses in Colab notebooks, which integrate code, figures, tables, and annotation in a single file. The code notebooks for each chapter may be copied, adapted, and reused in one's own analyses. The book also introduces the usage of machine learning predictive models using the Python sklearn package in the context of marketing research. This book is designed for three groups of readers: experienced marketing researchers who wish to learn to program in Python, coming from tools and languages such as R, SAS, or SPSS; analysts or students who already program in Python and wish to learn about marketing applications; and undergraduate or graduate marketing students with little or no programming background. It presumes only an introductory level of familiarity with formal statistics and contains a minimum of mathematics.
Python for Mathematics (Chapman & Hall/CRC The Python Series)
by Vincent KnightPython for Mathematics introduces readers to effective methods for doing mathematics using the Python programming language. Most programming texts introduce readers to the building blocks of programming and build up to using more sophisticated tools for a specific purpose, like doing particular mathematical tasks. This is akin to teaching someone how to forge metal so as to make a nail, and then slowly working up to using sophisticated power tools so as to actually build something. This book does things in a different way, by first getting readers to begin using and understanding the tools that are going to be helpful to them as mathematicians, and only then moving onto the granular details. In this way, the practical application of the tools can aid in the understanding of the theory.Features· Complete with engaging, practical exercises· Many useful and detailed coding examples· Suitable for undergraduates in mathematics, and other quantitative sciences· Empowers readers to design and create their own Python tools.
Python for Natural Language Processing: Programming with NumPy, scikit-learn, Keras, and PyTorch (Cognitive Technologies)
by Pierre M. NuguesSince the last edition of this book (2014), progress has been astonishing in all areas of Natural Language Processing, with recent achievements in Text Generation that spurred a media interest going beyond the traditional academic circles. Text Processing has meanwhile become a mainstream industrial tool that is used, to various extents, by countless companies. As such, a revision of this book was deemed necessary to catch up with the recent breakthroughs, and the author discusses models and architectures that have been instrumental in the recent progress of Natural Language Processing.As in the first two editions, the intention is to expose the reader to the theories used in Natural Language Processing, and to programming examples that are essential for a deep understanding of the concepts. Although present in the previous two editions, Machine Learning is now even more pregnant, having replaced many of the earlier techniques to process text. Many new techniques build on the availability of text. Using Python notebooks, the reader will be able to load small corpora, format text, apply the models through executing pieces of code, gradually discover the theoretical parts by possibly modifying the code or the parameters, and traverse theories and concrete problems through a constant interaction between the user and the machine. The data sizes and hardware requirements are kept to a reasonable minimum so that a user can see instantly, or at least quickly, the results of most experiments on most machines.The book does not assume a deep knowledge of Python, and an introduction to this language aimed at Text Processing is given in Ch. 2, which will enable the reader to touch all the programming concepts, including NumPy arrays and PyTorch tensors as fundamental structures to represent and process numerical data in Python, or Keras for training Neural Networks to classify texts. Covering topics like Word Segmentation and Part-of-Speech and Sequence Annotation, the textbook also gives an in-depth overview of Transformers (for instance, BERT), Self-Attention and Sequence-to-Sequence Architectures.
Python for Offensive PenTest: A practical guide to ethical hacking and penetration testing using Python
by Hussam KhraisYour one-stop guide to using Python, creating your own hacking tools, and making the most out of resources available for this programming languageKey FeaturesComprehensive information on building a web application penetration testing framework using PythonMaster web application penetration testing using the multi-paradigm programming language PythonDetect vulnerabilities in a system or application by writing your own Python scriptsBook DescriptionPython is an easy-to-learn and cross-platform programming language that has unlimited third-party libraries. Plenty of open source hacking tools are written in Python, which can be easily integrated within your script.This book is packed with step-by-step instructions and working examples to make you a skilled penetration tester. It is divided into clear bite-sized chunks, so you can learn at your own pace and focus on the areas of most interest to you. This book will teach you how to code a reverse shell and build an anonymous shell. You will also learn how to hack passwords and perform a privilege escalation on Windows with practical examples. You will set up your own virtual hacking environment in VirtualBox, which will help you run multiple operating systems for your testing environment.By the end of this book, you will have learned how to code your own scripts and mastered ethical hacking from scratch.What you will learnCode your own reverse shell (TCP and HTTP)Create your own anonymous shell by interacting with Twitter, Google Forms, and SourceForgeReplicate Metasploit features and build an advanced shellHack passwords using multiple techniques (API hooking, keyloggers, and clipboard hijacking)Exfiltrate data from your targetAdd encryption (AES, RSA, and XOR) to your shell to learn how cryptography is being abused by malwareDiscover privilege escalation on Windows with practical examplesCountermeasures against most attacksWho this book is forThis book is for ethical hackers; penetration testers; students preparing for OSCP, OSCE, GPEN, GXPN, and CEH; information security professionals; cybersecurity consultants; system and network security administrators; and programmers who are keen on learning all about penetration testing.
Python for Probability, Statistics, and Machine Learning
by José UnpingcoThis book covers thekey ideas that link probability, statistics, and machine learning illustratedusing Python modules in these areas. The entire text, including all thefigures and numerical results, is reproducible using the Python codes and theirassociated Jupyter/IPython notebooks, which are provided as supplementarydownloads. The author develops key intuitions in machine learning by workingmeaningful examples using multiple analytical methods and Python codes, therebyconnecting theoretical concepts to concrete implementations. Modern Pythonmodules like Pandas, Sympy, and Scikit-learn are applied to simulate andvisualize important machine learning concepts like the bias/variance trade-off,cross-validation, and regularization. Many abstract mathematical ideas, such asconvergence in probability theory, are developed and illustrated with numericalexamples. This book is suitable for anyone with an undergraduate-levelexposure to probability, statistics, or machine learning and with rudimentaryknowledge of Python programming.
Python for Probability, Statistics, and Machine Learning
by José UnpingcoThis textbook, fully updated to feature Python version 3.7, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. The update features full coverage of Web-based scientific visualization with Bokeh Jupyter Hub; Fisher Exact, Cohen’s D and Rank-Sum Tests; Local Regression, Spline, and Additive Methods; and Survival Analysis, Stochastic Gradient Trees, and Neural Networks and Deep Learning. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for classes in probability, statistics, or machine learning and requires only rudimentary knowledge of Python programming.
Python for Probability, Statistics, and Machine Learning
by José UnpingcoThis book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras.This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.
Python for R Users: A Data Science Approach
by Ajay OhriThe definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of R to Python and Python to R. Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining—including supervised and unsupervised data mining methods—are treated in detail, as are time series forecasting, text mining, and natural language processing. • Features a quick-learning format with concise tutorials and actionable analytics • Provides command-by-command translations of R to Python and vice versa • Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages • Offers numerous comparative examples and applications in both programming languages • Designed for use for practitioners and students that know one language and want to learn the other • Supplies slides useful for teaching and learning either software on a companion website Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics. A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing.
Python for SAS Users: A SAS-Oriented Introduction to Python
by Randy Betancourt Sarah ChenBusiness users familiar with Base SAS programming can now learn Python by example. You will learn via examples that map SAS programming constructs and coding patterns into their Python equivalents. Your primary focus will be on pandas and data management issues related to analysis of data. It is estimated that there are three million or more SAS users worldwide today. As the data science landscape shifts from using SAS to open source software such as Python, many users will feel the need to update their skills. Most users are not formally trained in computer science and have likely acquired their skills programming SAS as part of their job. As a result, the current documentation and plethora of books and websites for learning Python are technical and not geared for most SAS users. Python for SAS Users provides the most comprehensive set of examples currently available. It contains over 200 Python scripts and approximately 75 SAS programs that are analogs to the Python scripts. The first chapters are more Python-centric, while the remaining chapters illustrate SAS and corresponding Python examples to solve common data analysis tasks such as reading multiple input sources, missing value detection, imputation, merging/combining data, and producing output. This book is an indispensable guide for integrating SAS and Python workflows. What You’ll Learn Quickly master Python for data analysis without using a trial-and-error approachUnderstand the similarities and differences between Base SAS and PythonBetter determine which language to use, depending on your needsObtain quick results Who This Book Is For SAS users, SAS programmers, data scientists, data scientist leaders, and Python users who need to work with SAS
Python for Scientists
by John M. StewartPython is a free, open source, easy-to-use software tool that offers a significant alternative to proprietary packages such as MATLAB and Mathematica. This book covers everything the working scientist needs to know to start using Python effectively. The author explains scientific Python from scratch, showing how easy it is to implement and test non-trivial mathematical algorithms and guiding the reader through the many freely available add-on modules. A range of examples, relevant to many different fields, illustrate the program's capabilities. In particular, readers are shown how to use pre-existing legacy code (usually in Fortran77) within the Python environment, thus avoiding the need to master the original code. Instead of exercises the book contains useful snippets of tested code which the reader can adapt to handle problems in their own field, allowing students and researchers with little computer expertise to get up and running as soon as possible.
Python for Scientists
by John M. StewartPython is a free, open source, easy-to-use software tool that offers a significant alternative to proprietary packages such as MATLAB and Mathematica. This book covers everything the working scientist needs to know to start using Python effectively. The author explains scientific Python from scratch, showing how easy it is to implement and test non-trivial mathematical algorithms and guiding the reader through the many freely available add-on modules. A range of examples, relevant to many different fields, illustrate the program's capabilities. In particular, readers are shown how to use pre-existing legacy code (usually in Fortran77) within the Python environment, thus avoiding the need to master the original code. Instead of exercises the book contains useful snippets of tested code which the reader can adapt to handle problems in their own field, allowing students and researchers with little computer expertise to get up and running as soon as possible.
Python for Secret Agents
by Steven F. LottIf you are a Python beginner who is looking to learn the language through interesting projects, this book is for you. A basic knowledge of programming and statistics is beneficial to get the most out of the book.
Python for Secret Agents - Second Edition
by Steven F. LottThis book is for Secret Agents who have some exposure to Python. Our focus is on the Field Agents who are ready to do more sophisticated and complex programming in Python. We'll stick to simple statistics for the most part. A steady hand with a soldering iron is not required, but a skilled field agent should be able to assemble a working Arduino circuit to gather their own sensor data.
Python for Security and Networking: Leverage Python modules and tools in securing your network and applications, 3rd Edition
by Jose Manuel OrtegaGain a firm, practical understanding of securing your network and utilize Python's packages to detect vulnerabilities in your applicationKey FeaturesDiscover security techniques to protect your network and systems using PythonCreate scripts in Python to automate security and pentesting tasksAnalyze traffic in a network and extract information using PythonBook DescriptionPython's latest updates add numerous libraries that can be used to perform critical security-related missions, including detecting vulnerabilities in web applications, taking care of attacks, and helping to build secure and robust networks that are resilient to them. This fully updated third edition will show you how to make the most of them and improve your security posture.The first part of this book will walk you through Python scripts and libraries that you'll use throughout the book. Next, you'll dive deep into the core networking tasks where you will learn how to check a network's vulnerability using Python security scripting and understand how to check for vulnerabilities in your network – including tasks related to packet sniffing. You'll also learn how to achieve endpoint protection by leveraging Python packages along with writing forensics scripts.The next part of the book will show you a variety of modern techniques, libraries, and frameworks from the Python ecosystem that will help you extract data from servers and analyze the security in web applications. You'll take your first steps in extracting data from a domain using OSINT tools and using Python tools to perform forensics tasks.By the end of this book, you will be able to make the most of Python to test the security of your network and applications.What you will learnProgram your own tools in Python that can be used in a Network Security processAutomate tasks of analysis and extraction of information from serversDetect server vulnerabilities and analyze security in web applicationsAutomate security and pentesting tasks by creating scripts with PythonUtilize the ssh-audit tool to check the security in SSH serversExplore WriteHat as a pentesting reports tool written in PythonAutomate the process of detecting vulnerabilities in applications with tools like FuxploiderWho this book is forThis Python book is for network engineers, system administrators, and other security professionals looking to overcome common networking and security issues using Python. You will also find this book useful if you're an experienced programmer looking to explore Python's full range of capabilities. A basic understanding of general programming structures as well as familiarity with the Python programming language is a prerequisite.
Python for Signal Processing
by José UnpingcoThis book covers the fundamental concepts in signal processing illustrated with Python code and made available via IPython Notebooks, which are live, interactive, browser-based documents that allow one to change parameters, redraw plots, and tinker with the ideas presented in the text. Everything in the text is computable in this format and thereby invites readers to "experiment and learn" as they read. The book focuses on the core, fundamental principles of signal processing. The code corresponding to this book uses the core functionality of the scientific Python toolchain that should remain unchanged into the foreseeable future. For those looking to migrate their signal processing codes to Python, this book illustrates the key signal and plotting modules that can ease this transition. For those already comfortable with the scientific Python toolchain, this book illustrates the fundamental concepts in signal processing and provides a gateway to further signal processing concepts.
Python for Software Design
by Allen B. DowneyA no-nonsense introduction to software design using the Python programming language. Written 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. The book includes a wide range of exercises, from short examples to substantial projects, so that students have ample opportunity to practise each new concept. Exercise solutions and code examples are available from thinkpython. com, along with Swampy, a suite of Python programs that is used in some of the exercises.
Python for Teenagers: Learn to Program like a Superhero!
by James R. PayneDiscover everything you need to know about Python to turn your passion of programming into a job you'll love. Fueled by fun and practical examples, this book gives high schoolers who want learn an easy programming language ideas for how to leverage them in the workforce. Start with the basics and before you know it, you'll be building your own web sites, doing white-hat hacking, finding code bugs and errors, and creating games, including using Python to roll characters for RPGs. Every chapter is relaxed and informal, like learning with a cool teacher all the time. Computers, phones and the web are your playground, and you'll be ready to join the party with your own content. Going beyond posts and uploads means learning to program, and Python is a great choice to get started. It's quick to learn, it's flexible, and if you want, it may get you a Python job that pays more than minimum wage when you're out of school. Python for Teenagers is the most fun you'll have while learning.What You'll LearnReview programming basics - you gotta start somewhereCode applications that follow directions and make decisionsUnderstand Classes and objects - when a program is a childMake games with graphics and animationWho This Book Is ForHigh schoolers who want learn an easy programming language.
Python for Teenagers: Learn to Program like a Superhero!
by James R. PayneThis book, which is designed for middle-school through college-aged students, will arm beginners with solid programming foundations they can carry throughout life. It uses fun and simple language (and programming examples) to teach the fundamentals needed to start the down path of becoming a programmer. Python is a highly flexible language, allowing developers to enter any number of technical fields and is a welcome addition to any resume. With its low learning curve, it makes a great introductory language, as new developers can take the coding fundamentals they learn in Python and apply them to any other language. The second edition builds upon the foundation of the first book, revising all the chapters where the language has changed, updating the commands, code, and examples to bring it up to date with the current version of Python. Since Python is the most popular programming language in the world and can be used in conjunction with other languages - across multiple platforms - it can increase the reader's ability to qualify for a wider range of jobs than other languages. Finally, Python is fun - something not every programming language can boast! What You Will Learn Install and configure PythonGrasp basic software development principles and syntaxUnderstand the best practices for coding in PythonCreate applications and debug code Who This Book Is For The book’s target audience is primarily middle school to college aged students looking to learn how to program computers and develop software. Older individuals and computer programmers who know other languages and want to add Python to their repertoire can also benefit from the book.
Python for Unix and Linux System Administration
by Jeremy Jones Noah GiftPython is an ideal language for solving problems, especially in Linux and Unix networks. With this pragmatic book, administrators can review various tasks that often occur in the management of these systems, and learn how Python can provide a more efficient and less painful way to handle them. Each chapter in Python for Unix and Linux System Administration presents a particular administrative issue, such as concurrency or data backup, and presents Python solutions through hands-on examples. Once you finish this book, you'll be able to develop your own set of command-line utilities with Python to tackle a wide range of problems. Discover how this language can help you: Read text files and extract information Run tasks concurrently using the threading and forking options Get information from one process to another using network facilities Create clickable GUIs to handle large and complex utilities Monitor large clusters of machines by interacting with SNMP programmatically Master the IPython Interactive Python shell to replace or augment Bash, Korn, or Z-Shell Integrate Cloud Computing into your infrastructure, and learn to write a Google App Engine Application Solve unique data backup challenges with customized scripts Interact with MySQL, SQLite, Oracle, Postgres, Django ORM, and SQLAlchemy With this book, you'll learn how to package and deploy your Python applications and libraries, and write code that runs equally well on multiple Unix platforms. You'll also learn about several Python-related technologies that will make your life much easier.
Python for Unix and Linux System Administration
by Noah Gift Jeremy M. JonesPython is an ideal language for solving problems, especially in Linux and Unix networks. With this pragmatic book, administrators can review various tasks that often occur in the management of these systems, and learn how Python can provide a more efficient and less painful way to handle them.Each chapter in Python for Unix and Linux System Administration presents a particular administrative issue, such as concurrency or data backup, and presents Python solutions through hands-on examples. Once you finish this book, you'll be able to develop your own set of command-line utilities with Python to tackle a wide range of problems. Discover how this language can help you:Read text files and extract informationRun tasks concurrently using the threading and forking optionsGet information from one process to another using network facilitiesCreate clickable GUIs to handle large and complex utilitiesMonitor large clusters of machines by interacting with SNMP programmaticallyMaster the IPython Interactive Python shell to replace or augment Bash, Korn, or Z-ShellIntegrate Cloud Computing into your infrastructure, and learn to write a Google App Engine ApplicationSolve unique data backup challenges with customized scriptsInteract with MySQL, SQLite, Oracle, Postgres, Django ORM, and SQLAlchemyWith this book, you'll learn how to package and deploy your Python applications and libraries, and write code that runs equally well on multiple Unix platforms. You'll also learn about several Python-related technologies that will make your life much easier.
Python for Water and Environment (Innovations in Sustainable Technologies and Computing)
by Anil Kumar Manabendra SahariaThis textbook delves into the practical applications of surface and groundwater hydrology, as well as the environment. The Part I, "Practical Python for a Water and Environment Professional," guides readers through setting up a scientific computing environment and conducting exploratory data analysis and visualization using reproducible workflows. The Part II, "Statistical Modeling in Hydrology," covers regression models, time series analysis, and common hypothesis testing. The Part III, "Surface and Subsurface Water," illustrates the use of Python in understanding key concepts related to seepage, groundwater, and surface water flows. Lastly, the Part IV, "Environmental Applications," demonstrates the application of Python in the study of various contaminant transport phenomena.
Python for the Life Sciences: A Gentle Introduction to Python for Life Scientists
by Alexander Lancaster Gordon WebsterTreat yourself to a lively, intuitive, and easy-to-follow introduction to computer programming in Python. The book was written specifically for biologists with little or no prior experience of writing code - with the goal of giving them not only a foundation in Python programming, but also the confidence and inspiration to start using Python in their own research. Virtually all of the examples in the book are drawn from across a wide spectrum of life science research, from simple biochemical calculations and sequence analysis, to modeling the dynamic interactions of genes and proteins in cells, or the drift of genes in an evolving population. Best of all, Python for the Life Sciences shows you how to implement all of these projects in Python, one of the most popular programming languages for scientific computing. If you are a life scientist interested in learning Python to jump-start your research, this is the book for you.What You'll Learn Write Python scripts to automate your lab calculationsSearch for important motifs in genome sequencesUse object-oriented programming with PythonStudy mining interaction network data for patternsReview dynamic modeling of biochemical switchesWho This Book Is For Life scientists with little or no programming experience, including undergraduate and graduate students, postdoctoral researchers in academia and industry, medical professionals, and teachers/lecturers.“A comprehensive introduction to using Python for computational biology... A lovely book with humor and perspective” -- John Novembre, Associate Professor of Human Genetics, University of Chicago and MacArthur Fellow “Fun, entertaining, witty and darn useful. A magical portal to the big data revolution” -- Sandro Santagata, Assistant Professor in Pathology, Harvard Medical School “Alex and Gordon’s enthusiasm for Python is contagious” -- Glenys Thomson Professor of Integrative Biology, University of California, Berkeley
Python für Dummies (Für Dummies)
by Johannes C. Hofmeister Horst SchneiderPython ist eine der beliebtesten und vielseitigsten Programmiersprachen überhaupt. Für viele Entwickler führt deshalb kein Weg an Python vorbei. Schöpfen Sie mit diesem Buch aus dem reichen Erfahrungsschatz zweier langjähriger Softwareentwickler. Sie lernen zunächst die Syntax der Sprache und vertiefen dann das Erlernte anhand von konkreten Aufgabenstellungen. Für den Blick über den Tellerrand sorgen Kapitel zu Programmierparadigmen, Code-Qualität, Test-Ansätzen und Dokumentation. Jede Menge Tipps und Tricks und ein breites Spektrum an Beispielen lassen Sie zu einem wahren Python-Profi werden.