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IPython Interactive Computing and Visualization Cookbook
Over 100 hands-on recipes to sharpen your skills in high-performance numerical computing and data science with PythonAbout This BookLeverage the new features of the IPython notebook for interactive web-based big data analysis and visualizationBecome an expert in high-performance computing and visualization for data analysis and scientific modelingA comprehensive coverage of scientific computing through many hands-on, example-driven recipes with detailed, step-by-step explanationsWho This Book Is ForIntended to anyone interested in numerical computing and data students, researchers, teachers, engineers, analysts, hobbyists... Basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.
What You Will LearnCode better by writing high-quality, readable, and well-tested programs; profiling and optimizing your code, and conducting reproducible interactive computing experimentsMaster all of the new features of the IPython notebook, including the interactive HTML/JavaScript widgetsAnalyze data with Bayesian and frequentist statistics (Pandas, PyMC, and R), and learn from data with machine learning (scikit-learn)Gain valuable insights into signals, images, and sounds with SciPy, scikit-image, and OpenCVLearn how to write blazingly fast Python programs with NumPy, PyTables, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA and OpenCL), parallel IPython, MPI, and many moreIn DetailIPython is at the heart of the Python scientific stack. With its widely acclaimed web-based notebook, IPython is today an ideal gateway to data analysis and numerical computing in Python.
IPython Interactive Computing and Visualization Cookbook contains many ready-to-use focused recipes for high-performance scientific computing and data analysis. The first part covers programming techniques, including code quality and reproducibility; code optimization; high-performance computing through dynamic 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 LearnCode better by writing high-quality, readable, and well-tested programs; profiling and optimizing your code, and conducting reproducible interactive computing experimentsMaster all of the new features of the IPython notebook, including the interactive HTML/JavaScript widgetsAnalyze data with Bayesian and frequentist statistics (Pandas, PyMC, and R), and learn from data with machine learning (scikit-learn)Gain valuable insights into signals, images, and sounds with SciPy, scikit-image, and OpenCVLearn how to write blazingly fast Python programs with NumPy, PyTables, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA and OpenCL), parallel IPython, MPI, and many moreIn DetailIPython is at the heart of the Python scientific stack. With its widely acclaimed web-based notebook, IPython is today an ideal gateway to data analysis and numerical computing in Python.
IPython Interactive Computing and Visualization Cookbook contains many ready-to-use focused recipes for high-performance scientific computing and data analysis. The first part covers programming techniques, including code quality and reproducibility; code optimization; high-performance computing through dynamic 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.
948 pages, Kindle Edition
First published January 1, 2014
28 people are currently reading
45 people want to read
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Displaying 1 - 7 of 7 reviews
May 10, 2015
A cookbook that seemingly targets IPython but provides very few IPython specific recipes.
When I pick up a cookbook for technology X, I expect the recipes to be grounded in technology X. While recipes can be about how to set up the technology or use the technology to accomplish a task, the book has to be about the technology. In this case (as with "Learning IPython for Interactive Computing and Data Visualization"), recipes focus on libraries such as NumPy, Pandas, Matplotlib, and SymPy, etc. and not specific to IPython.
Even if the book were shorter, I would have been happier if it focused on the abilities and features of IPython and left recipes specific to other technologies to cookbooks dedicated to those technologies. To elaborate, if a recipe involving NumPy can be applied independent of IPython, then it is a NumPy specific recipe. On the other hand, if a recipe involving NumPy relies on a feature of IPython and cannot be employed outside of IPython (e.g., python shell), then that is a IPython recipe. Moreover, I suspect such a recipe would be applicable to other technologies that can be used with IPython. If so, the book should illustrate such general use of a recipe.
In short, while I may return to this book, I will only do so for very few chapters that are specific to IPython.
When I pick up a cookbook for technology X, I expect the recipes to be grounded in technology X. While recipes can be about how to set up the technology or use the technology to accomplish a task, the book has to be about the technology. In this case (as with "Learning IPython for Interactive Computing and Data Visualization"), recipes focus on libraries such as NumPy, Pandas, Matplotlib, and SymPy, etc. and not specific to IPython.
Even if the book were shorter, I would have been happier if it focused on the abilities and features of IPython and left recipes specific to other technologies to cookbooks dedicated to those technologies. To elaborate, if a recipe involving NumPy can be applied independent of IPython, then it is a NumPy specific recipe. On the other hand, if a recipe involving NumPy relies on a feature of IPython and cannot be employed outside of IPython (e.g., python shell), then that is a IPython recipe. Moreover, I suspect such a recipe would be applicable to other technologies that can be used with IPython. If so, the book should illustrate such general use of a recipe.
In short, while I may return to this book, I will only do so for very few chapters that are specific to IPython.
January 16, 2015
This book provides a valid continuation from the author previous IPython book, giving further examples for visualization and advanced computing. The book is written with a simple but intermediate skill level so if you feel like reading gibberish you should try reading “Learning IPython for Interactive Computing and Data Visualization” so you can get a proper introduction to the basics of IPython(the books provide a very light introduction that may not be sufficient if you never handled the language). The book purpose is to give practical examples and code samples for giving you new ideas and maybe solve you some problems you've found previously so use it to learn by example
March 12, 2015
A few of the GUI elements used in a couple of recipes are deprecated with the release of IPython 3+, but apart from that minor detail (which all books suffer from), this is an excellent resource.
September 1, 2024
Only for someone who knows absolutely nothing about the recipe they are looking up. For example, for simulating a stochastic differential equation, they provide a low-quality algorithm (that generates a warning due to a bad use of legend) based on Euler-Maruyama and six links to wikipedia pages.
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December 7, 2021IPython is great and this book covers really well what you can do with IPython and IPython notebook.
Now, GitHub renders IPython notebooks, it's going to be even greater.
Now, GitHub renders IPython notebooks, it's going to be even greater.
October 23, 2015
Good reference for common scientific python problems.
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February 2, 2018006.78 R827 2014
Displaying 1 - 7 of 7 reviews