IPython Interactive Computing and Visualization Cookbook
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- 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 DetailsLanguage : English
Paperback : 423 pages [ 235mm x 191mm ]
Release Date : August 2014
ISBN : 1783284811
ISBN 13 : 9781783284818
Author(s) : Cyrille Rossant
Topics and Technologies : All Books, Application Development, Big Data and Business Intelligence, Open Source
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What you will learn from this book
- Visualize big data with Matplotlib and OpenGL, and create interactive plots in the IPython notebook
- Simulate deterministic and stochastic dynamical systems in Python
- Analyze data with Bayesian or frequentist statistics (Pandas, PyMC, R), and learn from actual data through machine learning (scikit-learn)
- Gain valuable insights into signals, images, audio processing with SciPy and scikits, and computer vision with 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
- Familiarize yourself with the math in Python with SymPy and Sage: algebra, analysis, logic, graphs, geometry, and probability theory
IPython offers an extended interactive shell that is far more powerful than the default Python console. It brings the power of the Python programming language to a command-line interface that extends the standard system shell. In addition, the IPython notebook brings reproducibility to interactive computing by handling most of the low-level details and gives you a streamlined way of parallelizing its computations.
Designed to help you learn the most advanced interactive computing techniques of IPython, this books starts off by covering the basics of programming techniques, namely code quality, reproducibility, and code optimization. You will then venture into high-performance computing through dynamic compilation, parallel computing, and graphics card programming.
This book gives you interactive access to high-performance numerical libraries like NumPy and SciPy for fast and efficient computations on vectors and matrices. You also get a walkthrough of the key concepts and terms related to statistics, machine learning, signal and image processing, and dynamical systems.
You will then move on to learn about the implementation of common numerical optimization algorithms, such as [color=#0040FF] the least-square method, curve fitting, and stochastic global optimization algorithms. The book also covers deterministic dynamical systems including ordinary differential equations, vector fields, physical systems, and partial differential equations.
You will finish by plunging into the depths of SymPy and SciPy to do symbolic and numerical mathematical computations such as differentiation and integration, series summations, polynomials, and linear algebra algorithms.
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.
Who this book is for
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.