Author: Kurt W. Smith
Publisher: "O'Reilly Media, Inc."
Build software that combines Python’s expressivity with the performance and control of C (and C++). It’s possible with Cython, the compiler and hybrid programming language used by foundational packages such as NumPy, and prominent in projects including Pandas, h5py, and scikits-learn. In this practical guide, you’ll learn how to use Cython to improve Python’s performance—up to 3000x— and to wrap C and C++ libraries in Python with ease. Author Kurt Smith takes you through Cython’s capabilities, with sample code and in-depth practice exercises. If you’re just starting with Cython, or want to go deeper, you’ll learn how this language is an essential part of any performance-oriented Python programmer’s arsenal. Use Cython’s static typing to speed up Python code Gain hands-on experience using Cython features to boost your numeric-heavy Python Create new types with Cython—and see how fast object-oriented programming in Python can be Effectively organize Cython code into separate modules and packages without sacrificing performance Use Cython to give Pythonic interfaces to C and C++ libraries Optimize code with Cython’s runtime and compile-time profiling tools Use Cython’s prange function to parallelize loops transparently with OpenMP
A practical and a fast-paced guide that gives you all the information you need to start programming using Cython.This book is for developers who love C/C++ for low latency and speed but who also require the ability to add more dynamic features to applications both fast and reliably. It will also show you how you can get new applications off the ground by reusing Python libraries to get started.
High Performance Python
Author: Micha Gorelick, Ian Ozsvald
Publisher: "O'Reilly Media, Inc."
Your Python code may run correctly, but you need it to run faster. By exploring the fundamental theory behind design choices, this practical guide helps you gain a deeper understanding of Python’s implementation. You’ll learn how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. How can you take advantage of multi-core architectures or clusters? Or build a system that can scale up and down without losing reliability? Experienced Python programmers will learn concrete solutions to these and other issues, along with war stories from companies that use high performance Python for social media analytics, productionized machine learning, and other situations. Get a better grasp of numpy, Cython, and profilers Learn how Python abstracts the underlying computer architecture Use profiling to find bottlenecks in CPU time and memory usage Write efficient programs by choosing appropriate data structures Speed up matrix and vector computations Use tools to compile Python down to machine code Manage multiple I/O and computational operations concurrently Convert multiprocessing code to run on a local or remote cluster Solve large problems while using less RAM
An exciting, easy-to-follow guide illustrating the techniques to boost the performance of Python code, and their applications with plenty of hands-on examples. If you are a programmer who likes the power and simplicity of Python and would like to use this language for performance-critical applications, this book is ideal for you. All that is required is a basic knowledge of the Python programming language. The book will cover basic and advanced topics so will be great for you whether you are a new or a seasoned Python developer.
Learn how to use Python to create efficient applicationsAbout This Book* Identify the bottlenecks in your applications and solve them using the best profiling techniques* Write efficient numerical code in NumPy, Cython, and Pandas* Adapt your programs to run on multiple processors and machines with parallel programmingWho This Book Is ForThe book is aimed at Python developers who want to improve the performance of their application. Basic knowledge of Python is expectedWhat You Will Learn* Write efficient numerical code with the NumPy and Pandas libraries* Use Cython and Numba to achieve native performance* Find bottlenecks in your Python code using profilers* Write asynchronous code using Asyncio and RxPy* Use Tensorflow and Theano for automatic parallelism in Python* Set up and run distributed algorithms on a cluster using Dask and PySparkIn DetailPython is a versatile language that has found applications in many industries. The clean syntax, rich standard library, and vast selection of third-party libraries make Python a wildly popular language.Python High Performance is a practical guide that shows how to leverage the power of both native and third-party Python libraries to build robust applications.The book explains how to use various profilers to find performance bottlenecks and apply the correct algorithm to fix them. The reader will learn how to effectively use NumPy and Cython to speed up numerical code. The book explains concepts of concurrent programming and how to implement robust and responsive applications using Reactive programming. Readers will learn how to write code for parallel architectures using Tensorflow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark.By the end of the book, readers will have learned to achieve performance and scale from their Python applications.Style and approachA step-by-step practical guide filled with real-world use cases and examples
Learn the fundamentals of Cython to extend the legacy of your applications About This Book Learn how to extend C applications with pure Python code Get more from Python – you'll not only learn Cython, you'll also unlock a greater understanding of how to harness Python Packed with tips and tricks that make Cython look easy, dive into this accessible programming guide and find out what happens when you bring C and Python together! Who This Book Is For This book is for developers who are familiar with the basics of C and Python programming and wish to learn Cython programming to extend their applications. What You Will Learn Reuse Python logging in C Make an IRC bot out of your C application Extend an application so you have a web server for rest calls Practice Cython against your C++ code Discover tricks to work with Python ConfigParser in C Create Python bindings for native libraries Find out about threading and concurrency related to GIL Expand Terminal Multiplexer Tmux with Cython In Detail Cython is a hybrid programming language used to write C extensions for Python language. Combining the practicality of Python and speed and ease of the C language it's an exciting language worth learning if you want to build fast applications with ease. This new edition of Learning Cython Programming shows you how to get started, taking you through the fundamentals so you can begin to experience its unique powers. You'll find out how to get set up, before exploring the relationship between Python and Cython. You'll also look at debugging Cython, before moving on to C++ constructs, Caveat on C++ usage, Python threading and GIL in Cython. Finally, you'll learn object initialization and compile time, and gain a deeper insight into Python 3, which will help you not only become a confident Cython developer, but a much more fluent Python developer too. Style and approach This practical and a fast-paced guide gives you all the information you need to start programming using Cython.
Author: Leo (Liang-Huan) Chin, Tanmay Dutta
Publisher: Packt Publishing Ltd
Boost your scientific and analytic capabilities in no time at all by discovering how to build real-world applications with NumPy About This Book Optimize your Python scripts with powerful NumPy modules Explore the vast opportunities to build outstanding scientific/ analytical modules by yourself Packed with rich examples to help you master NumPy arrays and universal functions Who This Book Is For If you are an experienced Python developer who intends to drive your numerical and scientific applications with NumPy, this book is for you. Prior experience or knowledge of working with the Python language is required. What You Will Learn Manipulate the key attributes and universal functions of NumPy Utilize matrix and mathematical computation using linear algebra modules Implement regression and curve fitting for models Perform time frequency / spectral density analysis using the Fourier Transform modules Collate with the distutils and setuptools modules used by other Python libraries Establish Cython with NumPy arrays Write extension modules for NumPy code using the C API Build sophisticated data structures using NumPy array with libraries such as Panda and Scikits In Detail In today's world of science and technology, it's all about speed and flexibility. When it comes to scientific computing, NumPy tops the list. NumPy gives you both the speed and high productivity you need. This book will walk you through NumPy using clear, step-by-step examples and just the right amount of theory. We will guide you through wider applications of NumPy in scientific computing and will then focus on the fundamentals of NumPy, including array objects, functions, and matrices, each of them explained with practical examples. You will then learn about different NumPy modules while performing mathematical operations such as calculating the Fourier Transform; solving linear systems of equations, interpolation, extrapolation, regression, and curve fitting; and evaluating integrals and derivatives. We will also introduce you to using Cython with NumPy arrays and writing extension modules for NumPy code using the C API. This book will give you exposure to the vast NumPy library and help you build efficient, high-speed programs using a wide range of mathematical features. Style and approach This quick guide will help you get to grips with the nitty-gritties of NumPy using with practical programming examples. Each topic is explained in both theoretical and practical ways with hands-on examples providing you efficient way of learning and adequate knowledge to support your professional work.
Python in Practice
Author: Mark Summerfield
Publisher: Pearson Education
Winner of the 2014 Jolt Award for "Best Book" "Whether you are an experienced programmer or are starting your career, Python in Practice is full of valuable advice and example to help you improve your craft by thinking about problems from different perspectives, introducing tools, and detailing techniques to create more effective solutions." --Doug Hellmann, Senior Developer, DreamHost If you're an experienced Python programmer, Python in Practice will help you improve the quality, reliability, speed, maintainability, and usability of all your Python programs. Mark Summerfield focuses on four key themes: design patterns for coding elegance, faster processing through concurrency and compiled Python (Cython), high-level networking, and graphics. He identifies well-proven design patterns that are useful in Python, illuminates them with expert-quality code, and explains why some object-oriented design patterns are irrelevant to Python. He also explodes several counterproductive myths about Python programming--showing, for example, how Python can take full advantage of multicore hardware. All examples, including three complete case studies, have been tested with Python 3.3 (and, where possible, Python 3.2 and 3.1) and crafted to maintain compatibility with future Python 3.x versions. All code has been tested on Linux, and most code has also been tested on OS X and Windows. All code may be downloaded atwww.qtrac.eu/pipbook.html. Coverage includes Leveraging Python's most effective creational, structural, and behavioral design patterns Supporting concurrency with Python's multiprocessing, threading, and concurrent.futures modules Avoiding concurrency problems using thread-safe queues and futures rather than fragile locks Simplifying networking with high-level modules, including xmlrpclib and RPyC Accelerating Python code with Cython, C-based Python modules, profiling, and other techniques Creating modern-looking GUI applications with Tkinter Leveraging today's powerful graphics hardware via the OpenGL API using pyglet and PyOpenGL
This guidebook provides Zen, Fourth Way, Tantric Yoga students, and others with important information for developing higher states of mental and spiritual being, gathered from many sources and organized into easy-to-study categories.
Learn to use IPython and Jupyter Notebook for your data analysis and visualization work. Key Features Leverage the Jupyter Notebook for interactive data science and visualization Become an expert in high-performance computing and visualization for data analysis and scientific modeling A comprehensive coverage of scientific computing through many hands-on, example-driven recipes with detailed, step-by-step explanations Book Description Python is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning. The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics. What you will learn Master all features of the Jupyter Notebook Code better: write high-quality, readable, and well-tested programs; profile and optimize your code; and conduct reproducible interactive computing experiments Visualize data and create interactive plots in the Jupyter Notebook Write blazingly fast Python programs with NumPy, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA), parallel IPython, Dask, and more Analyze data with Bayesian or frequentist statistics (Pandas, PyMC, and R), and learn from actual data through machine learning (scikit-learn) Gain valuable insights into signals, images, and sounds with SciPy, scikit-image, and OpenCV Simulate deterministic and stochastic dynamical systems in Python Familiarize yourself with math in Python using SymPy and Sage: algebra, analysis, logic, graphs, geometry, and probability theory Who this book is for This book is intended for anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, and hobbyists. A basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.
Measure, optimize, and improve the performance of your Python code with this easy-to-follow guide About This Book Master the do's and don'ts of Python performance programming Learn how to use exiting new tools that will help you improve your scripts A step-by-step, conceptual guide to teach you how to optimize and fine-tune your critical pieces of code Who This Book Is For If you're a Python developer looking to improve the speed of your scripts or simply wanting to take your skills to the next level, then this book is perfect for you. What You Will Learn Master code optimization step-by-step and learn how to use different tools Understand what a profiler is and how to read its output Interpret visual output from profiling tools and improve the performance of your script Use Cython to create fast applications using Python and C Take advantage of PyPy to improve performance of Python code Optimize number-crunching code with NumPy, Numba, Parakeet, and Pandas In Detail Simply knowing how to code is not enough; on mission-critical pieces of code, every bit of memory and every CPU cycle counts, and knowing how to squish every bit of processing power out of your code is a crucial and sought-after skill. Nowadays, Python is used for many scientific projects, and sometimes the calculations done in those projects require some serious fine-tuning. Profilers are tools designed to help you measure the performance of your code and help you during the optimization process, so knowing how to use them and read their output is very handy. This book starts from the basics and progressively moves on to more advanced topics. You'll learn everything from profiling all the way up to writing a real-life application and applying a full set of tools designed to improve it in different ways. In the middle, you'll stop to learn about the major profilers used in Python and about some graphic tools to help you make sense of their output. You'll then move from generic optimization techniques onto Python-specific ones, going over the main constructs of the language that will help you improve your speed without much of a change. Finally, the book covers some number-crunching-specific libraries and how to use them properly to get the best speed out of them. After reading this book, you will know how to take any Python code, profile it, find out where the bottlenecks are, and apply different techniques to remove them. Style and approach This easy-to-follow, practical guide will help you enhance your optimization skills by improving real-world code.
A practical hands-on guide which focuses on interactive programming, numerical computing, and data analysis with IPython.This book is for Python developers who use Python as a scripting language or for software development, and are interested in learning IPython for increasing their productivity during interactive sessions in the console. Knowledge of Python is required, whereas no knowledge of IPython is necessary.
Gain a thorough understanding of operating in a Python development environment, and some of the most important advanced topics with Daniel Arbuckle. This dynamic, concise book is full of real-world solutions for Python 3.6 problems, and advanced-level concepts such as reactive programming, microservices, ctypes and Cython. About This Book Covers the latest and advanced concepts of Python such as parallel processing with Python 3.6 Explore the Python language from its basic installation and setup to concepts such as reactive programming and microservices Get introduced to the mechanism for rewriting code in a compiled language along with ctypes and Cython tools Who This Book Is For If you are a programmer and are familiar with the basics of Python, and you want to broaden your knowledge base to develop projects better and faster, this book is for you. Even if you are not familiar with Python, Daniel Arbuckle's Mastering Python starts with the basics and takes you on a journey to become an expert in the technology. What You Will Learn Get to grips with the basics of operating in a Python development environment Build Python packages to efficiently create reusable code Become proficient at creating tools and utility programs in Python Use the Git version control system to protect your development environment from unwanted changes Harness the power of Python to automate other software Distribute computational tasks across multiple processors Handle high I/O loads with asynchronous I/O to get a smoother performance Take advantage of Python's metaprogramming and programmable syntax features Get acquainted with the concepts behind reactive programming and RxPy In Detail Daniel Arbuckle's Mastering Python covers the basics of operating in a Python development environment, before moving on to more advanced topics. Daniel presents you with real-world solutions to Python 3.6 and advanced-level concepts, such as reactive programming, microservices, ctypes, and Cython tools. You don't need to be familiar with the Python language to use this book, as Daniel starts with a Python primer. Throughout, Daniel highlights the major aspects of managing your Python development environment, shows you how to handle parallel computation, and helps you to master asynchronous I/O with Python 3.6 to improve performance. Finally, Daniel will teach you the secrets of metaprogramming and unit testing in Python, helping you acquire the perfect skillset to be a Python expert. Daniel will get you up to speed on everything from basic programming practices to high-end tools and techniques, things that will help set you apart as a successful Python programmer. Style and Approach Daniel Arbuckle's Mastering Python covers basic to advanced-level concepts in computer science. If you are a beginner, then Daniel will help you get started. If you are experienced, he will expand your knowledge base.
Author: Ivan Idris
Publisher: Packt Publishing Ltd
Written in Cookbook style, the code examples will take your Numpy skills to the next level. This book will take Python developers with basic Numpy skills to the next level through some practical recipes.
Python Data Analysis
Author: Armando Fandango
Publisher: Packt Publishing Ltd
Learn how to apply powerful data analysis techniques with popular open source Python modules About This Book Find, manipulate, and analyze your data using the Python 3.5 libraries Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code An easy-to-follow guide with realistic examples that are frequently used in real-world data analysis projects. Who This Book Is For This book is for programmers, scientists, and engineers who have the knowledge of Python and know the basics of data science. It is for those who wish to learn different data analysis methods using Python 3.5 and its libraries. This book contains all the basic ingredients you need to become an expert data analyst. What You Will Learn Install open source Python modules such NumPy, SciPy, Pandas, stasmodels, scikit-learn,theano, keras, and tensorflow on various platforms Prepare and clean your data, and use it for exploratory analysis Manipulate your data with Pandas Retrieve and store your data from RDBMS, NoSQL, and distributed filesystems such as HDFS and HDF5 Visualize your data with open source libraries such as matplotlib, bokeh, and plotly Learn about various machine learning methods such as supervised, unsupervised, probabilistic, and Bayesian Understand signal processing and time series data analysis Get to grips with graph processing and social network analysis In Detail Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries. Style and approach The book takes a very comprehensive approach to enhance your understanding of data analysis. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work. Packed with clear, easy to follow examples, this book will turn you into an ace data analyst in no time.