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Python Playground
Python is a powerful programming language that s easy to learn and fun to play with. But once you ve gotten a handle on the basics, what do you do next? Python Playground is a collection of imaginative programming projects that will inspire you to use Python to make art and music, build simulations of real-world phenomena, and interact with hardware like the Arduino and Raspberry Pi. You ll learn to use common Python tools and libraries like numpy, matplotlib, and pygame to do things like: Generate Spirograph-like patterns using parametric equations and the turtle module Create music on your computer by simulating frequency overtones Translate graphical images into ASCII art Write an autostereogram program that produces 3D images hidden beneath random patterns Make realistic animations with OpenGL shaders by exploring particle systems, transparency, and billboarding techniques Construct 3D visualizations using data from CT and MRI scans Build a laser show that responds to music
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Physik mit Python
Dieses Lehrbuch führt Sie anhand von physikalischen Fragestellungen aus der Mechanik in die Programmiersprache Python ein. Neben der reinen Simulation von physikalischen Systemen wird besonderes Augenmerk auf die Visualisierung von Ergebnissen und das Erstellen von Animationen gelegt. Mit zahlreichen Beispielen und Übungsaufgaben ermöglicht dieses Buch so den praktischen Einstieg in das wissenschaftliche Rechnen.Sie lernen Kurvenanpassungen durchzuführen sowie lineare und nicht-lineare Gleichungssysteme zu lösen, die bei der Behandlung von statischen Problemen auftreten. Auch die Lösung von Differentialgleichungen, die dynamische Systeme beschreiben, sowie Themen wie Fourier-Transformationen und Eigenwertprobleme kommen nicht zu kurz. Mithilfe der in diesem Buch vorgestellten Simulationsbeispiele vertiefen Sie darüber hinaus Ihr Verständnis der zugrundeliegenden Physik, indem Sie die physikalischen Gesetze algorithmisch umsetzen und physikalische Aufgaben simulieren, die weitaus komplexer sind als die üblichen mit Papier und Bleistift lösbaren Aufgaben.Alle im Buch vorgestellten Programme, die fertigen Animationen sowie die Lösungen zu den Übungsaufgaben werden online bereitgestellt.Die vorliegende zweite Auflage enthält zahlreiche Ergänzungen, Korrekturen und Anpassungen an die aktuellen Versionen von Python, NumPy, SciPy und Matplotlib. Darüber hinaus erwartet Sie ein zusätzliches Kapitel über objektorientierte Programmiermethoden. Ob Sie also Physik oder eine Ingenieurwissenschaft mit hohem physikalischem Anteil studieren, oder ob Sie unterrichten und Ihre Lehre durch Simulationen und Animationen anreichern möchten – dieses Buch ist dabei Ihr optimaler Begleiter!
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Mastering Social Media Mining with Python
Acquire and analyze data from all corners of the social web with PythonAbout This Book• Make sense of highly unstructured social media data with the help of the insightful use cases provided in this guide• Use this easy-to-follow, step-by-step guide to apply analytics to complicated and messy social data• This is your one-stop solution to fetching, storing, analyzing, and visualizing social media dataWho This Book Is ForThis book is for intermediate Python developers who want to engage with the use of public APIs to collect data from social media platforms and perform statistical analysis in order to produce useful insights from data. The book assumes a basic understanding of the Python Standard Library and provides practical examples to guide you toward the creation of your data analysis project based on social data.What You Will Learn• Interact with a social media platform via their public API with Python• Store social data in a convenient format for data analysis• Slice and dice social data using Python tools for data science• Apply text analytics techniques to understand what people are talking about on social media• Apply advanced statistical and analytical techniques to produce useful insights from data• Build beautiful visualizations with web technologies to explore data and present data productsIn DetailYour social media is filled with a wealth of hidden data – unlock it with the power of Python. Transform your understanding of your clients and customers when you use Python to solve the problems of understanding consumer behavior and turning raw data into actionable customer insights.This book will help you acquire and analyze data from leading social media sites. It will show you how to employ scientific Python tools to mine popular social websites such as Facebook, Twitter, Quora, and more. Explore the Python libraries used for social media mining, and get the tips, tricks, and insider insight you need to make the most of them. Discover how to develop data mining tools that use a social media API, and how to create your own data analysis projects using Python for clear insight from your social data.Style and approachThis practical, hands-on guide will help you learn everything you need to perform data mining for social media. Throughout the book, we take an example-oriented approach to use Python for data analysis and provide useful tips and tricks that you can use in day-to-day tasks.
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Python Cookbook
If you need help writing programs in Python 3, or want to update older Python 2 code, this book is just the ticket. Packed with practical recipes written and tested with Python 3.3, this unique cookbook is for experienced Python programmers who want to focus on modern tools and idioms. Inside, you'll find complete recipes for more than a dozen topics, covering the core Python language as well as tasks common to a wide variety of application domains. Each recipe contains code samples you can use in your projects right away, along with a discussion about how and why the solution works. Topics include: Data Structures and Algorithms Strings and Text Numbers, Dates, and Times Iterators and Generators Files and I/O Data Encoding and Processing Functions Classes and Objects Metaprogramming Modules and Packages Network and Web Programming Concurrency Utility Scripting and System Administration Testing, Debugging, and Exceptions C Extensions
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Python for Excel Users
Excel is a mainstay of the modern workplace, but it isn't up to every task. Python for Excel Users offers a better way, showing how relatively simple programming can greatly boost productivity by streamlining workflows and adding new tools to the reader's toolbox - no programming experience required. Uses include: Generating beautiful data visualizations, Creating interactive reports and dashboards, Tracking collaborative projects. This book leverages what readers already know about Excel to introduce useful coding applications. Practical examples illustrate how programming skills can be immediately applied to the reader's day-to-day work.
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Scaling Python with Ray
Serverless computing enables developers to concentrate solely on their applications rather than worry about where they've been deployed. With the Ray general-purpose serverless implementation in Python, programmers and data scientists can hide servers, implement stateful applications, support direct communication between tasks, and access hardware accelerators.In this book, authors Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while avoiding single points of failure and manual scheduling. If your data processing has grown beyond what a single computer can handle, this book is for you.Written by experienced software architecture practitioners, Scaling Python with Ray is ideal for software architects and developers eager to explore successful case studies and learn more about decision and measurement effectiveness. This book covers distributed processing (the pure Python implementation of serverless) and shows you how to:Implement stateful applications with Ray actorsBuild workflow management in RayUse Ray as a unified platform for batch and streamingImplement advanced data processing with RayApply microservices with Ray platformImplement reliable Ray applications
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Hands-On Financial Trading with Python
Learn algorithmic trading with Python by building one complete strategy, from market data to automated execution. Explore machine learning, AI signals, and rigorous backtestingKey FeaturesFollow one volatility tail-hedge strategy from market data to cloud deployment, and build more that show a real edgeExplore time-series models, machine learning, and AI-driven alpha signal generationChallenge backtests with walk-forward analysis and detect lookahead bias and overfittingBook DescriptionGo beyond learning Python libraries in isolation. This book teaches you the complete workflow professionals use to build systematic trading strategies.You start by acquiring, exploring, and cleaning market data, handling missing values, misaligned time series, and noise. From there, you form trading hypotheses, generate signals, and define entry and exit rules. You evaluate risk-adjusted performance and challenge your backtests through out-of-sample testing and walk-forward analysis. Next, you explore time-series models, machine learning for trading, and AI-driven alpha signal generation. Finally, you build a deployable strategy repository and automate execution in the cloud. One volatility tail-hedge strategy runs from raw data to cloud deployment, and you build five further strategies from published research, covering momentum, regime detection, economic indicators, and sector rotation, each tested to confirm that its signal does real work.Drawing on 25 years of institutional trading experience, the book shows you how to spot lookahead bias and overfitting and distinguish promising signals from noise. You work with VectorBT and QuantStats, with PythonAnywhere for deployment. The book includes all the code, and by the end you have working strategies and a repeatable framework for algorithmic trading with PythonWhat you will learnAcquire, clean, and denoise financial time-series dataUse exploratory analysis to develop trading hypothesesBuild momentum, mean-reversion, and time-series strategiesApply machine learning and AI to alpha signal generationValidate backtests with out-of-sample and walk-forward testingDetect lookahead bias, survivorship bias, and overfittingEvaluate strategies using risk-adjusted performance metricsBuild a deployable repository and automate cloud executionWho this book is forThis book is for traders going systematic, data scientists entering quantitative finance, and portfolio managers automating their workflows. Whether you are building your first complete strategy or moving beyond isolated Python scripts, you share one frustration: books that teach tools but not how to put them together. This book gives you that missing piece.You should be comfortable with basic Python, including functions, loops, and working in Jupyter notebooks, and with finance fundamentals such as returns, volatility, and risk.
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Financial Modelling in Python
"Fletcher and Gardner have created a comprehensive resource that will be of interest not only to those working in the field of finance, but also to those using numerical methods in other fields such as engineering, physics, and actuarial mathematics. By showing how to combine the high-level elegance, accessibility, and flexibility of Python, with the low-level computational efficiency of C++, in the context of interesting financial modeling problems, they have provided an implementation template which will be useful to others seeking to jointly optimize the use of computational and human resources. They document all the necessary technical details required in order to make external numerical libraries available from within Python, and they contribute a useful library of their own, which will significantly reduce the start-up costs involved in building financial models. This book is a must read for all those with a need to apply numerical methods in the valuation of financial claims." –David Louton, Professor of Finance, Bryant University This book is directed at both industry practitioners and students interested in designing a pricing and risk management framework for financial derivatives using the Python programming language. It is a practical book complete with working, tested code that guides the reader through the process of building a flexible, extensible pricing framework in Python. The pricing frameworks' loosely coupled fundamental components have been designed to facilitate the quick development of new models. Concrete applications to real-world pricing problems are also provided. Topics are introduced gradually, each building on the last. They include basic mathematical algorithms, common algorithms from numerical analysis, trade, market and event data model representations, lattice and simulation based pricing, and model development. The mathematics presented is kept simple and to the point. The book also provides a host of information on practical technical topics such as C++/Python hybrid development (embedding and extending) and techniques for integrating Python based programs with Microsoft Excel.
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