Articles by "Python"

Showing posts with label Python. Show all posts

Friday, July 26, 2024 , ,
Felix Zumstein ... 335 pages - Language: ‎English - Publisher: ‎O'Reilly Media; (April, 2021) - ISBN-10: 1492081000 - ISBN-13: 978-1492081005.

While Excel remains ubiquitous in the business world, recent Microsoft feedback forums are full of requests to include Python as an Excel scripting language. In fact, it's the top feature requested. What makes this combination so compelling? In this hands-on guide, Felix Zumstein--creator of xlwings, a popular open source package for automating Excel with Python--shows experienced Excel users how to integrate these two worlds efficiently.

Excel has added quite a few new capabilities over the past couple of years, but its automation language, VBA, stopped evolving a long time ago. Many Excel power users have already adopted Python for daily automation tasks. This guide gets you started

Use Python without extensive programming knowledge + Get started with modern tools, including Jupyter notebooks and Visual Studio code + Use pandas to acquire, clean, and analyze data and replace typical Excel calculations + Automate tedious tasks like consolidation of Excel workbooks and production of Excel reports + Use xlwings to build interactive Excel tools that use Python as a calculation engine + Connect Excel to databases and CSV files and fetch data from the internet using Python code + Use Python as a single tool to replace VBA, Power Query, and Power Pivot.
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Saturday, July 20, 2024 , ,
David Mertz ... 288 pages - Language: English - Publisher: ‎Addison-Wesley Professional; (November, 2023).

Python is one of the most widely used programming languages in the world. It is used everywhere from primary school education to workaday web development, to the most advanced scientific research institutes of the world. However, like all programming languages, Python has a collection of "Pythonic" ways of accomplishing tasks that are easy to overlook, especially when habits are borrowed wholesale from work in other programming languages. Better Python Code is a guide to Pythonic programming. The book presents common mistakes that Python developers make--even Python developers who have used the language for years--often because Python sometimes presents "attractive nuisances." Throughout, the book is a guide to better programming in the core Python language.

Each section shows a concrete but concise example of some misunderstanding or bad habit in action. Each section contains a description of what is wrong with the sample code and a suggestion for one or more better ways to code equivalent functionality without the initial pitfall. Every pitfall addressed in this book reflects foibles, errors, and misunderstandings that the author as seen in concrete, widely used code bases written by experienced developers, over his 25 years of writing Python. The sections of this book each present some mistake, pitfall, or foible that developers can easily fall into, and are accompanied by descriptions of ways to avoid making them. At times those solutions simply involve a minor change in “spelling,” but in most cases they require a nuance of thought and design in your code. Many of the discussions do something else as well…
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Monday, July 26, 2021 , ,
Brett Slatkin ... 256 pages - Publisher: Addison-Wesley Professional; (March, 2015) ... Language: English - ISBN-10: 0134034287 - ISBN-13: 978-0134034287.

It's easy to start writing code with Python: that's why the language is so immensely popular. However, Python has unique strengths, charms, and expressivity that can be hard to grasp at first -- as well as hidden pitfalls that can easily trip you up if you aren't aware of them. Effective Python will help you harness the full power of Python to write exceptionally robust, efficient, maintainable, and well-performing code. Utilizing the concise, scenario-driven style pioneered in Scott Meyers's best-selling Effective C++, Brett Slatkin brings together 59 Python best practices, tips, shortcuts, and realistic code examples from expert programmers. Through realistic examples, Slatkin uncovers little-known Python quirks, intricacies, and idioms that powerfully impact code behavior and performance. You'll learn how to choose the most efficient and effective way to accomplish key tasks when multiple options exist, and how to write code that's easier to understand, maintain, and improve. Drawing on his deep understanding of Python's capabilities, Slatkin offers practical advice for each major area of development with both Python 3.x and Python 2.x. 

Coverage includes: Algorithms + Objects + Concurrency + Collaboration + Built-in modules + Production techniques + And more. Each section contains specific, actionable guidelines organized into items, each with carefully worded advice supported by detailed technical arguments and illuminating examples. Using Effective Python, you can systematically improve all the Python code you write: not by blindly following rules or mimicking incomprehensible idioms, but by gaining a deep understanding of the technical reasons why they make sense.
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Saturday, January 30, 2021 , , ,
Daniel Chen ... 416 pages ... Publisher: Addison-Wesley Professional; (December, 2017) ... Language: English - ISBN-10: 0134546938.

Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes.

Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem.: Work with DataFrames and Series, and import or export data + Create plots with matplotlib, seaborn, and pandas + Combine datasets and handle missing data + Reshape, tidy, and clean datasets so they’re easier to work with + Convert data types and manipulate text strings + Apply functions to scale data manipulations + Aggregate, transform, and filter large datasets with groupby + Leverage Pandas’ advanced date and time capabilities + Fit linear models using statsmodels and scikit-learn libraries + Use generalized linear modeling to fit models with different response variables + Compare multiple models to select the “best” + Regularize to overcome overfitting and improve performance + Use clustering in unsupervised machine learning.
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Thursday, November 12, 2020 , , ,
Paul A. Zandbergen ... 420 pages - ISBN-10: 1589484991 - ISBN-13: 978-1589484993 ... Publisher: Esri Press; (July, 2020) - Language: English.

Python Scripting for ArcGIS Pro starts with the fundamentals of Python programming and then dives into how to write useful Python scripts that work with spatial data in ArcGIS Pro. Learn how to execute geoprocessing tools, describe, create and update data, as well as execute a number of specialized tasks. See how to write simple, custom scripts that will automate your ArcGIS Pro workflows.

Some of the key topics you will learn include: Python fundamentals + Setting up a Python editor + Automating geoprocessing tasks using ArcPy + Exploring and manipulating spatial and tabular data + Working with geometries using cursors + Working with rasters and map algebra + Map scripting + Debugging and error handling.

Helpful “points to remember,” key terms, and review questions are included at the end of each chapter to reinforce your understanding of Python. Corresponding data and exercises are available online. Whether you want to learn Python or already have some experience, Python Scripting for ArcGIS Pro is the comprehensive, hands-on book for learning the versatility of Python coding as an approach to solving problems and increasing your productivity in ArcGIS Pro. Follow the step-by-step instruction and common workflow guidance for automating tasks and scripting with Python.
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Wednesday, November 11, 2020 , , , ,
Abhishek Kumar Pandey, Pramod Singh Rathore, S. Balamurugan ... 282 pages - AmazonSIN: B07WFZZ2TB ... Publisher: BPB Publications; (June, 2019) - Language: English.

Machine learning is mostly sought in the research field and has become an integral part of many research projects nowadays including commercial applications, as well as academic research. Application of machine learning ranges from finding friends on social networking sites to medical diagnosis and even satellite processing. In this book, we have made an honest effort to make the concepts of machine learning easy and give basic programs in MATLAB right from the installation part. Although the real-time application of machine learning is endless, however, the basic concepts and algorithms are discussed using MATLAB language so that not only graduation students but also researchers are benefitted from it. 

What will you learn: ● Pre-requisites to machine learning ● Finding natural patterns in data ● Building classification methods ● Data pre-processing in Python ● Building regression models ● Creating neural networks ● Deep learning
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Monday, September 28, 2020 , , , ,
Laura Graesser, Wah Loon Keng ... 416 pages - ISBN-13: 978-0135172384 - ISBN-10: 0135172381 ... Publisher: Addison-Wesley Professional; (December, 2019) - Language: English.

The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice: Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games–such as Go, Atari games, and DotA 2–to robotics.

Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python: Understand each key aspect of a deep RL problem + Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) + Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) + Understand how algorithms can be parallelized synchronously and asynchronously + Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work + Explore algorithm benchmark results with tuned hyperparameters + Understand how deep RL environments are designed.
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Tuesday, July 28, 2020 , , ,
Eihab B. M. Bashier ... 348 pages - Publisher: CRC Press; (March, 2020) ... Language: English - ISBN-10: 0367076691 - ISBN-13: 978-0367076696.

Practical Numerical and Scientific Computing with MATLAB and Python concentrates on the practical aspects of numerical analysis and linear and non-linear programming. It discusses the methods for solving different types of mathematical problems using MATLAB and Python. Although the book focuses on the approximation problem rather than on error analysis of mathematical problems, it provides practical ways to calculate errors. The book is divided into three parts, covering topics in numerical linear algebra, methods of interpolation, numerical differentiation and integration, solutions of differential equations, linear and non-linear programming problems, and optimal control problems. This book has the following advantages: It adopts the programming languages, MATLAB and Python, which are widely used among academics, scientists, and engineers, for ease of use and contain many libraries covering many scientific and engineering fields. + It contains topics that are rarely found in other numerical analysis books, such as ill-conditioned linear systems and methods of regularization to stabilize their solutions, nonstandard finite differences methods for solutions of ordinary differential equations, and the computations of the optimal controls. It provides a practical explanation of how to apply these topics using MATLAB and Python. + It discusses software libraries to solve mathematical problems, such as software Gekko, pulp, and pyomo. These libraries use Python for solutions to differential equations and static and dynamic optimization problems. + Most programs in the book can be applied in versions prior to MATLAB 2017b and Python 3.7.4 without the need to modify these programs. This book is aimed at newcomers and middle-level students, as well as members of the scientific community who are interested in solving math problems using MATLAB or Python.
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Friday, July 24, 2020 , , ,
Erik Lee Nylen, Pascal Wallisch ... 368 pages - Publisher: Academic Press; (April, 2017) ... Language: English - ISBN-10: 0128040432 - ISBN-13: 978-0128040430.

A Primer with MATLAB® and Python™ present important information on the emergence of the use of Python, a more general purpose option to MATLAB, the preferred computation language for scientific computing and analysis in neuroscience. This book addresses the snake in the room by providing a beginner’s introduction to the principles of computation and data analysis in neuroscience, using both Python and MATLAB, giving readers the ability to transcend platform tribalism and enable coding versatility.
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Sunday, July 12, 2020 , , ,
Zed Shaw ... 320 pages - Publisher: Addison-Wesley Professional; (June, 2017) ... Language: English - ISBN-10: 0134692888 - ISBN-13: 978-0134692883.

In Learn Python 3 the Hard Way, you’ll learn Python by working through 52 brilliantly crafted exercises. Read them. Type their code precisely. (No copying and pasting!) Fix your mistakes. Watch the programs run. As you do, you’ll learn how a computer works; what good programs look like; and how to read, write, and think about code. Zed then teaches you even more in 5+ hours of video where he shows you how to break, fix, and debug your code—live, as he’s doing the exercises.

Install a complete Python environment + Organize and write code + Fix and break code
Basic mathematics + Variables + Strings and text + Interact with users + Work with files + Looping and logic + Data structures using lists and dictionaries + Program design + Object-oriented programming + Inheritance and composition + Modules, classes, and objects + Python packaging + Automated testing + Basic game development + Basic web development.

It’ll be hard at first. But soon, you’ll just get it—and that will feel great! This course will reward you for every minute you put into it. Soon, you’ll know one of the world’s most powerful, popular programming languages. You’ll be a Python programmer.  This Book Is Perfect For: Total beginners with zero programming experience + Junior developers who know one or two languages + Returning professionals who haven’t written code in years + Seasoned professionals looking for a fast, simple, crash course in Python 3.
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Tuesday, July 07, 2020 , ,
Mark Fenner ... 592 pages - Publisher: Addison-Wesley Professional; (August, 2019) ... Language: English - ISBN-10: 0134845625 - ISBN-13: 978-0134845623.

The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python: Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use.

Understand machine learning algorithms, models, and core machine learning concepts + Classify examples with classifiers, and quantify examples with regressors + Realistically assess performance of machine learning systems + Use feature engineering to smooth rough data into useful forms + Chain multiple components into one system and tune its performance + Apply machine learning techniques to images and text + Connect the core concepts to neural networks and graphical models + Leverage the Python scikit-learn library and other powerful tools.
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Thursday, June 25, 2020 , ,
Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda ... 332 pages - Publisher: O'Reilly Media; (July, 2018) ... Language: English - ISBN-10: 1491963042 - ISBN-13: 978-1491963043.

From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems.

Preprocess and vectorize text into high-dimensional feature representations + Perform document classification and topic modeling + Steer the model selection process with visual diagnostics + Extract key phrases, named entities, and graph structures to reason about data in text + Build a dialog framework to enable chatbots and language-driven interaction + Use Spark to scale processing power and neural networks to scale model complexity.
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Tuesday, June 23, 2020 , , ,
Ryan Mitchell ... 308 pages - Publisher: O'Reilly Media; 2nd edition (April, 2018) ... Language: English - ISBN-10: 1491985577 - ISBN-13: 978-1491985571.

If programming is magic then web scraping is surely a form of wizardry. By writing a simple automated program, you can query web servers, request data, and parse it to extract the information you need. The expanded edition of this practical book not only introduces you web scraping, but also serves as a comprehensive guide to scraping almost every type of data from the modern web. Part I focuses on web scraping mechanics: using Python to request information from a web server, performing basic handling of the server’s response, and interacting with sites in an automated fashion. Part II explores a variety of more specific tools and applications to fit any web scraping scenario you’re likely to encounter. Parse complicated HTML pages + Develop crawlers with the Scrapy framework + Learn methods to store data you scrape + Read and extract data from documents + Clean and normalize badly formatted data + Read and write natural languages + Crawl through forms and logins + Scrape JavaScript and crawl through APIs + Use and write image-to-text software + Avoid scraping traps and bot blockers + Use scrapers to test your website.
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Tuesday, June 16, 2020 , ,
Ben Stephenson ... 219 pages - Publisher: Springer; 2nd edition (July, 2019) ... Language: English - ISBN-10: 3030188728 - ISBN-13: 978-3030188726.

This student-friendly textbook encourages the development of programming skills through active practice by focusing on exercises that support hands-on learning. The Python Workbook provides a compendium of 186 exercises, spanning a variety of academic disciplines and everyday situations. Solutions to selected exercises are also provided, supported by brief annotations that explain the technique used to solve the problem, or highlight a specific point of Python syntax. This enhanced new edition has been thoroughly updated and expanded with additional exercises, along with concise introductions that outline the core concepts needed to solve them. The exercises and solutions require no prior background knowledge, beyond the material covered in a typical introductory Python programming course.

Features: Uses an accessible writing style and easy-to-follow structure; includes a mixture of classic exercises from the fields of computer science and mathematics, along with exercises that connect to other academic disciplines; presents the solutions to approximately half of the exercises; provides annotations alongside the solutions, which explain the approach taken to solve the problem and relevant aspects of Python syntax; offers a variety of exercises of different lengths and difficulties; contains exercises that encourage the development of programming skills using if statements, loops, basic functions, lists, dictionaries, files, and recursive functions. Undergraduate students enrolled in their first programming course and wishing to enhance their programming abilities will find the exercises and solutions provided in this book to be ideal for their needs.
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Wednesday, May 13, 2020 , , ,
Seth Weidman ... 253 pages - Publisher: O'Reilly Media; (September, 2019) ... Language: English - AmazonSIN: B07XL53Y4C.

With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You’ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way. Author Seth Weidman shows you how neural networks work using a first principles approach. You’ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you’ll be set up for success on all future deep learning projects.

This book provides: Extremely clear and thorough mental models—accompanied by working code examples and mathematical explanations—for understanding neural networks + Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework + Working implementations and clear-cut explanations of convolutional and recurrent neural networks + Implementation of these neural network concepts using the popular PyTorch framework.
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Monday, May 11, 2020 , , , ,
Eugene Charniak ... 192 pages - Publisher: The MIT Press; (January, 2019) ... Language: English - AmazonSIN: B07PGRZXN8.

This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach.

Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.
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Sunday, May 10, 2020 , ,
Jesús Rogel-Salazar ... 420 pages - Publisher: Chapman and Hall/CRC; (May, 2020) ... Language: English - AmazonSIN: B0883XB13B.

Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications.

Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science + Uses Python throughout + Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs + Focuses on the practical use of the tools rather than on lengthy explanations + Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path.

The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users’ fingertips in the form of an iPhone app.
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Sunday, April 26, 2020 , , ,
Allen B. Downey ... 210 pages - Publisher: O'Reilly Media; (October, 2013) ... Language: English - ISBN-10: 1449370780 - ISBN-13: 978-1449370787.

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. Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this book’s computational approach helps you get a solid start. Use your existing programming skills to learn and understand Bayesian statistics + Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing + Get started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockey + Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.
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Tuesday, April 07, 2020 , , , ,
Laura Igual, Santi Segui ... 218 pages - Publisher: Springer; (March, 2017) ... Language: English - ISBN-10: 3319500163 - ISBN-13: 978-331950016.

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.
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Monday, April 06, 2020 , , ,
John M. Stewart ... 230 pages - Publisher: Cambridge University Press; (August, 2014) ... Language: English - ISBN-10: 1107686423 - ISBN-13: 978-1107686427.

Python 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.
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