A comprehensive tutorial covering the state-of-the-art methods you can utilize in IPython, including various real-world examples to help you learn in a practical and effective manner. The book illustrates topics in applied mathematics, scientific modeling, and statistical learning.
This book is intended for anyone who wants to use Python as a scripting language for general purpose programming and data analysis. You will find the book particularly useful if you're an experienced Python user looking to take the next step, or if you want to quickly get up to speed with IPython's unique features for data visualization.
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Cyrille Rossant, PhD, is a neuroscience researcher and software engineer at University College London. He is a graduate of École Normale Supérieure, Paris, where he studied mathematics and computer science. He has also worked at Princeton University and Collège de France. While working on data science and software engineering projects, he gained experience in numerical computing, parallel computing, and high-performance data visualization.He is the author of Learning IPython for Interactive Computing and Data Visualization, Second Edition, Packt Publishing.
Harness IPython for powerful scientific computing and Python data visualization with this collection of more than 100 practical data science recipes Key Features Leverage the new features of the IPython notebook for interactive web-based big data analysis 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 IPython 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 experiments Master all of the new features of the IPython notebook, including the interactive HTML/JavaScript widgets Analyze 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 OpenCV Learn how to write blazingly fast Python programs with NumPy, PyTables, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA and OpenCL), parallel IPython, MPI, and many more Who this book is for This book is for web developers who want to build the next generation of state-of-the-art mobile and desktop web applications with Angular. This book does not require you to have prior exposure to either Angular 1.x, 2 or 4, although comprehensive knowledge of JavaScript is assumed.
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