
IPython Interactive Computing and Visualization CookbookBy: Cyrille Rossant Publication Date: 20140924 Number of pages: 423 ISBN10: 1783284811 ISBN13: 9781783284818 Over 100 handson recipes to sharpen your skills in highperformance numerical computing and data science with PythonAbout This BookLeverage the new features of the IPython notebook for interactive webbased big data analysis and visualizationBecome an expert in highperformance computing and visualization for data analysis and scientific modelingA comprehensive coverage of scientific computing through many handson, exampledriven recipes with detailed, stepbystep explanationsWho This Book Is For Intended to anyone interested in numerical computing and data science: 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 Learn Code better by writing highquality, readable, and welltested 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 (scikitlearn) Gain valuable insights into signals, images, and sounds with SciPy, scikitimage, 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 In DetailIPython is at the heart of the Python scientific stack. With its widely acclaimed webbased notebook, IPython is today an ideal gateway to data analysis and numerical computing in Python. IPython Interactive Computing and Visualization Cookbook contains many readytouse focused recipes for highperformance scientific computing and data analysis. The first part covers programming techniques, including code quality and reproducibility; code optimization; highperformance 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. 