Key Features

Leverage the Jupyter Notebook for interactive data science 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 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 NotebookCode better: write high-quality, readable, and well-tested programs; profile and optimize your code; and conduct reproducible interactive computing experimentsVisualize data and create interactive plots in the Jupyter NotebookWrite blazingly fast Python programs with NumPy, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA), parallel IPython, Dask, and moreAnalyze 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 OpenCVSimulate deterministic and stochastic dynamical systems in PythonFamiliarize 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.

Table of Contents

A Tour of Interactive Computing with Jupyter and IPythonBest Practices in Interactive ComputingMastering the Jupyter NotebookProfiling and OptimizationHigh-Performance ComputingData VisualizationStatistical Data AnalysisMachine LearningNumerical OptimizationSignal ProcessingImage and Audio ProcessingDeterministic Dynamical SystemsStochastic Dynamical SystemsGraphs, Geometry, and Geographic Information SystemsSymbolic and Numerical Mathematics

About the Author

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 has 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, the prequel of this cookbook.

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