IPython Interactive Computing and Visualization Cookbook.

By: Rossant, CyrillePublisher: Olton : Packt Publishing, Limited, 2014Copyright date: ©2014Description: 1 online resource (684 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9781783284825Subject(s): Python (Computer program language)Genre/Form: Electronic books. Additional physical formats: Print version:: IPython Interactive Computing and Visualization CookbookDDC classification: 005.133 LOC classification: QA76.73.P98 -- .R67 2014ebOnline resources: Click to View
Contents:
Intro -- IPython Interactive Computing and Visualization Cookbook -- Table of Contents -- IPython Interactive Computing and Visualization Cookbook -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Support files, eBooks, discount offers, and more -- Why Subscribe? -- Free Access for Packt account holders -- Preface -- What this book is -- What this book covers -- Part 1 - Advanced High-Performance Interactive Computing -- Part 2 - Standard Methods in Data Science and Applied Mathematics -- What you need for this book -- Installing Python -- GitHub repositories -- Who this book is for -- Conventions -- Reader feedback -- Customer support -- Downloading the example code -- Downloading the color images -- Errata -- Piracy -- Questions -- 1. A Tour of Interactive Computing with IPython -- Introduction -- What is IPython? -- A brief historical retrospective on Python as a scientific environment -- What's new in IPython 2.0? -- Roadmap for IPython 3.0 and 4.0 -- References -- Introducing the IPython notebook -- Getting ready -- How to do it... -- There's more... -- See also -- Getting started with exploratory data analysis in IPython -- How to do it... -- There's more... -- See also -- Introducing the multidimensional array in NumPy for fast array computations -- How to do it... -- How it works... -- There's more... -- See also -- Creating an IPython extension with custom magic commands -- How to do it... -- How it works... -- The InteractiveShell class -- Loading an extension -- There's more... -- See also -- Mastering IPython's configuration system -- How to do it... -- How it works... -- Configurables -- Magics -- There's more... -- See also -- Creating a simple kernel for IPython -- Getting ready -- How to do it... -- How it works... -- There's more... -- 2. Best Practices in Interactive Computing -- Introduction.
Choosing (or not) between Python 2 and Python 3 -- How to do it... -- Main differences in Python 3 compared to Python 2 -- Python 2 or Python 3? -- Supporting both Python 2 and Python 3 -- Using 2to3 -- Writing code that works in Python 2 and Python 3 -- There's more... -- See also -- Efficient interactive computing workflows with IPython -- How to do it... -- The IPython terminal -- IPython and text editor -- The IPython notebook -- Integrated Development Environments -- There's more... -- See also -- Learning the basics of the distributed version control system Git -- Getting ready -- How to do it… -- Creating a local repository -- Cloning a remote repository -- How it works… -- There's more… -- See also -- A typical workflow with Git branching -- Getting ready -- How to do it… -- Stashing -- How it works… -- There's more… -- See also -- Ten tips for conducting reproducible interactive computing experiments -- How to do it… -- How it works… -- There's more... -- See also -- Writing high-quality Python code -- How to do it... -- How it works... -- There's more... -- See also -- Writing unit tests with nose -- Getting ready -- How to do it... -- How it works... -- There's more... -- Test coverage -- Workflows with unit testing -- Unit testing and continuous integration -- Debugging your code with IPython -- How to do it... -- The post-mortem mode -- Step-by-step debugging -- There's more... -- GUI debuggers -- 3. Mastering the Notebook -- Introduction -- What is the notebook? -- The notebook ecosystem -- Architecture of the IPython notebook -- Connecting multiple clients to one kernel -- Security in notebooks -- References -- Teaching programming in the notebook with IPython blocks -- Getting ready -- How to do it... -- There's more... -- Converting an IPython notebook to other formats with nbconvert -- Getting ready -- How to do it...
How it works... -- There's more... -- Adding custom controls in the notebook toolbar -- How to do it... -- There's more... -- See also -- Customizing the CSS style in the notebook -- Getting ready -- How to do it... -- There's more... -- See also -- Using interactive widgets - a piano in the notebook -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Creating a custom JavaScript widget in the notebook - a spreadsheet editor for pandas -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Processing webcam images in real time from the notebook -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- 4. Profiling and Optimization -- Introduction -- Evaluating the time taken by a statement in IPython -- How to do it... -- How it works... -- There's more... -- See also -- Profiling your code easily with cProfile and IPython -- How to do it... -- How it works... -- Premature optimization is the root of all evil -- There's more... -- See also -- Profiling your code line-by-line with line_profiler -- Getting ready -- How do to it... -- How it works... -- There's more... -- Tracing the step-by-step execution of a Python program -- See also -- Profiling the memory usage of your code with memory_profiler -- Getting ready -- How to do it... -- How it works... -- There's more... -- Using memory_profiler for standalone Python programs -- Using the %memit magic command in IPython -- Other tools -- See also -- Understanding the internals of NumPy to avoid unnecessary array copying -- Getting ready -- How to do it... -- How it works... -- Why are NumPy arrays efficient? -- What is the difference between in-place and implicit-copy operations? -- Why can't some arrays be reshaped without a copy? -- What are NumPy broadcasting rules? -- There's more... -- See also.
Using stride tricks with NumPy -- Getting ready -- How to do it... -- How it works... -- See also -- Implementing an efficient rolling average algorithm with stride tricks -- Getting ready -- How to do it... -- See also -- Making efficient array selections in NumPy -- Getting ready -- How to do it... -- How it works... -- There's more... -- Processing huge NumPy arrays with memory mapping -- How to do it... -- How it works... -- There's more... -- See also -- Manipulating large arrays with HDF5 and PyTables -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Manipulating large heterogeneous tables with HDF5 and PyTables -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- 5. High-performance Computing -- Introduction -- CPython and concurrent programming -- Compiler-related installation instructions -- Linux -- Mac OS X -- Windows -- Python 32-bit -- Python 64-bit -- DLL hell -- References -- Accelerating pure Python code with Numba and just-in-time compilation -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- Accelerating array computations with Numexpr -- Getting ready -- How to do it… -- How it works... -- See also -- Wrapping a C library in Python with ctypes -- Getting ready -- How to do it… -- How it works… -- There's more… -- Accelerating Python code with Cython -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- Optimizing Cython code by writing less Python and more C -- How to do it… -- How it works… -- There's more… -- See also -- Releasing the GIL to take advantage of multicore processors with Cython and OpenMP -- Getting ready -- How to do it… -- How it works… -- See also -- Writing massively parallel code for NVIDIA graphics cards (GPUs) with CUDA -- Getting ready -- How to do it... -- How it works….
There's more… -- See also -- Writing massively parallel code for heterogeneous platforms with OpenCL -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- Distributing Python code across multiple cores with IPython -- How to do it… -- How it works… -- There's more… -- Dependent parallel tasks -- Alternative parallel computing solutions -- References -- See also -- Interacting with asynchronous parallel tasks in IPython -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- Parallelizing code with MPI in IPython -- Getting ready -- How to do it… -- How it works… -- See also -- Trying the Julia language in the notebook -- Getting ready -- How to do it… -- How it works… -- There's more… -- 6. Advanced Visualization -- Introduction -- Making nicer matplotlib figures with prettyplotlib -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- Creating beautiful statistical plots with seaborn -- Getting ready -- How to do it… -- There's more… -- See also -- Creating interactive web visualizations with Bokeh -- Getting ready -- How to do it… -- There's more… -- See also -- Visualizing a NetworkX graph in the IPython notebook with D3.js -- Getting ready -- How to do it… -- There's more… -- See also -- Converting matplotlib figures to D3.js visualizations with mpld3 -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- Getting started with Vispy for high-performance interactive data visualizations -- Getting ready -- How to do it… -- How it works… -- There's more… -- Vispy for scientific visualization -- 7. Statistical Data Analysis -- Introduction -- What is statistical data analysis? -- A bit of vocabulary -- Exploration, inference, decision, and prediction -- Univariate and multivariate methods -- Frequentist and Bayesian methods.
Parametric and nonparametric inference methods.
Summary: 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.
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Intro -- IPython Interactive Computing and Visualization Cookbook -- Table of Contents -- IPython Interactive Computing and Visualization Cookbook -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Support files, eBooks, discount offers, and more -- Why Subscribe? -- Free Access for Packt account holders -- Preface -- What this book is -- What this book covers -- Part 1 - Advanced High-Performance Interactive Computing -- Part 2 - Standard Methods in Data Science and Applied Mathematics -- What you need for this book -- Installing Python -- GitHub repositories -- Who this book is for -- Conventions -- Reader feedback -- Customer support -- Downloading the example code -- Downloading the color images -- Errata -- Piracy -- Questions -- 1. A Tour of Interactive Computing with IPython -- Introduction -- What is IPython? -- A brief historical retrospective on Python as a scientific environment -- What's new in IPython 2.0? -- Roadmap for IPython 3.0 and 4.0 -- References -- Introducing the IPython notebook -- Getting ready -- How to do it... -- There's more... -- See also -- Getting started with exploratory data analysis in IPython -- How to do it... -- There's more... -- See also -- Introducing the multidimensional array in NumPy for fast array computations -- How to do it... -- How it works... -- There's more... -- See also -- Creating an IPython extension with custom magic commands -- How to do it... -- How it works... -- The InteractiveShell class -- Loading an extension -- There's more... -- See also -- Mastering IPython's configuration system -- How to do it... -- How it works... -- Configurables -- Magics -- There's more... -- See also -- Creating a simple kernel for IPython -- Getting ready -- How to do it... -- How it works... -- There's more... -- 2. Best Practices in Interactive Computing -- Introduction.

Choosing (or not) between Python 2 and Python 3 -- How to do it... -- Main differences in Python 3 compared to Python 2 -- Python 2 or Python 3? -- Supporting both Python 2 and Python 3 -- Using 2to3 -- Writing code that works in Python 2 and Python 3 -- There's more... -- See also -- Efficient interactive computing workflows with IPython -- How to do it... -- The IPython terminal -- IPython and text editor -- The IPython notebook -- Integrated Development Environments -- There's more... -- See also -- Learning the basics of the distributed version control system Git -- Getting ready -- How to do it… -- Creating a local repository -- Cloning a remote repository -- How it works… -- There's more… -- See also -- A typical workflow with Git branching -- Getting ready -- How to do it… -- Stashing -- How it works… -- There's more… -- See also -- Ten tips for conducting reproducible interactive computing experiments -- How to do it… -- How it works… -- There's more... -- See also -- Writing high-quality Python code -- How to do it... -- How it works... -- There's more... -- See also -- Writing unit tests with nose -- Getting ready -- How to do it... -- How it works... -- There's more... -- Test coverage -- Workflows with unit testing -- Unit testing and continuous integration -- Debugging your code with IPython -- How to do it... -- The post-mortem mode -- Step-by-step debugging -- There's more... -- GUI debuggers -- 3. Mastering the Notebook -- Introduction -- What is the notebook? -- The notebook ecosystem -- Architecture of the IPython notebook -- Connecting multiple clients to one kernel -- Security in notebooks -- References -- Teaching programming in the notebook with IPython blocks -- Getting ready -- How to do it... -- There's more... -- Converting an IPython notebook to other formats with nbconvert -- Getting ready -- How to do it...

How it works... -- There's more... -- Adding custom controls in the notebook toolbar -- How to do it... -- There's more... -- See also -- Customizing the CSS style in the notebook -- Getting ready -- How to do it... -- There's more... -- See also -- Using interactive widgets - a piano in the notebook -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Creating a custom JavaScript widget in the notebook - a spreadsheet editor for pandas -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Processing webcam images in real time from the notebook -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- 4. Profiling and Optimization -- Introduction -- Evaluating the time taken by a statement in IPython -- How to do it... -- How it works... -- There's more... -- See also -- Profiling your code easily with cProfile and IPython -- How to do it... -- How it works... -- Premature optimization is the root of all evil -- There's more... -- See also -- Profiling your code line-by-line with line_profiler -- Getting ready -- How do to it... -- How it works... -- There's more... -- Tracing the step-by-step execution of a Python program -- See also -- Profiling the memory usage of your code with memory_profiler -- Getting ready -- How to do it... -- How it works... -- There's more... -- Using memory_profiler for standalone Python programs -- Using the %memit magic command in IPython -- Other tools -- See also -- Understanding the internals of NumPy to avoid unnecessary array copying -- Getting ready -- How to do it... -- How it works... -- Why are NumPy arrays efficient? -- What is the difference between in-place and implicit-copy operations? -- Why can't some arrays be reshaped without a copy? -- What are NumPy broadcasting rules? -- There's more... -- See also.

Using stride tricks with NumPy -- Getting ready -- How to do it... -- How it works... -- See also -- Implementing an efficient rolling average algorithm with stride tricks -- Getting ready -- How to do it... -- See also -- Making efficient array selections in NumPy -- Getting ready -- How to do it... -- How it works... -- There's more... -- Processing huge NumPy arrays with memory mapping -- How to do it... -- How it works... -- There's more... -- See also -- Manipulating large arrays with HDF5 and PyTables -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Manipulating large heterogeneous tables with HDF5 and PyTables -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- 5. High-performance Computing -- Introduction -- CPython and concurrent programming -- Compiler-related installation instructions -- Linux -- Mac OS X -- Windows -- Python 32-bit -- Python 64-bit -- DLL hell -- References -- Accelerating pure Python code with Numba and just-in-time compilation -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- Accelerating array computations with Numexpr -- Getting ready -- How to do it… -- How it works... -- See also -- Wrapping a C library in Python with ctypes -- Getting ready -- How to do it… -- How it works… -- There's more… -- Accelerating Python code with Cython -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- Optimizing Cython code by writing less Python and more C -- How to do it… -- How it works… -- There's more… -- See also -- Releasing the GIL to take advantage of multicore processors with Cython and OpenMP -- Getting ready -- How to do it… -- How it works… -- See also -- Writing massively parallel code for NVIDIA graphics cards (GPUs) with CUDA -- Getting ready -- How to do it... -- How it works….

There's more… -- See also -- Writing massively parallel code for heterogeneous platforms with OpenCL -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- Distributing Python code across multiple cores with IPython -- How to do it… -- How it works… -- There's more… -- Dependent parallel tasks -- Alternative parallel computing solutions -- References -- See also -- Interacting with asynchronous parallel tasks in IPython -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- Parallelizing code with MPI in IPython -- Getting ready -- How to do it… -- How it works… -- See also -- Trying the Julia language in the notebook -- Getting ready -- How to do it… -- How it works… -- There's more… -- 6. Advanced Visualization -- Introduction -- Making nicer matplotlib figures with prettyplotlib -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- Creating beautiful statistical plots with seaborn -- Getting ready -- How to do it… -- There's more… -- See also -- Creating interactive web visualizations with Bokeh -- Getting ready -- How to do it… -- There's more… -- See also -- Visualizing a NetworkX graph in the IPython notebook with D3.js -- Getting ready -- How to do it… -- There's more… -- See also -- Converting matplotlib figures to D3.js visualizations with mpld3 -- Getting ready -- How to do it… -- How it works… -- There's more… -- See also -- Getting started with Vispy for high-performance interactive data visualizations -- Getting ready -- How to do it… -- How it works… -- There's more… -- Vispy for scientific visualization -- 7. Statistical Data Analysis -- Introduction -- What is statistical data analysis? -- A bit of vocabulary -- Exploration, inference, decision, and prediction -- Univariate and multivariate methods -- Frequentist and Bayesian methods.

Parametric and nonparametric inference methods.

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.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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