An edition of IPython Interactive Computing and Visualization Cookbook (2014)

IPython Interactive Computing and Visualization Cookbook

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Cover of: IPython Interactive Computing and Visualization Cookbook by Cyrille Rossant, Cyrille Rossant
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An edition of IPython Interactive Computing and Visualization Cookbook (2014)

IPython Interactive Computing and Visualization Cookbook

by

With its widely acclaimed web-based notebook, IPython is an ideal gateway to data analysis and numerical computing in Python. This book contains many ready-to-use focused recipes for high-performance scientific computing and data analysis. You will learn how to: code 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 insight into signals, images, and sounds with SciPy, scikit-image, and OpenCV; write blazingly fast Python programs with NumPy, PyTables, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA and OpenCL), parallel IPython, MPI, and many more. --

Publish Date
Publisher
Packt Publishing
Language
English
Pages
512

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Previews available in: English

Subjects

Python (Computer program language), Python (Langage de programmation), COMPUTERS, General, Command languages (Computer science), Information visualization, Interactive computer systems, Langages de programmation, Techniques d'imagerie, Technologie de l'information
Edition Availability
Cover of: IPython Interactive Computing and Visualization Cookbook
IPython Interactive Computing and Visualization Cookbook: Over 100 Hands-On Recipes to Sharpen Your Skills in High-performance Numerical Computing and Data Science in the Jupyter Notebook
2018, de Gruyter GmbH, Walter
in English
Cover of: IPython Interactive Computing and Visualization Cookbook
IPython Interactive Computing and Visualization Cookbook
2014, Packt Publishing
in English

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Book Details

Table of Contents

Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: A Tour of Interactive Computing with IPython; Introduction; Introducing the IPython notebook; Getting started with exploratory data analysis in IPython; Introducing the multidimensional array in NumPy for fast array computations; Creating an IPython extension with custom magic commands; Mastering IPython''s configuration system; Creating a simple kernel for IPython; Chapter 2: Best Practices in Interactive Computing; Introduction.
Choosing (or not) between Python 2 and Python 3Efficient interactive computing workflows with IPython; Learning the basics of the distributed version control system Git; A typical workflow with Git branching; Ten tips for conducting reproducible interactive computing experiments; Writing high-quality Python code; Writing unit tests with nose; Debugging your code with IPython; Chapter 3: Mastering the Notebook; Introduction; Teaching programming in the notebook with IPython blocks; Converting an IPython notebook to other formats with nbconvert; Adding custom controls in the notebook toolbar.
Customizing the CSS style in the notebookUsing interactive widgets
a piano in the notebook; Creating a custom JavaScript widget in the notebook
a spreadsheet editor for pandas; Processing webcam images in real time from the notebook; Chapter 4: Profiling and Optimization; Introduction; Evaluating the time taken by a statement in IPython; Profiling your code easily with cProfile and IPython; Profiling your code line-by-line with line_profiler; Profiling the memory usage of your code with memory_profiler; Understanding the internals of NumPy to avoid unnecessary array copying.
Using stride tricks with NumPyImplementing an efficient rolling average algorithm with stride tricks; Making efficient array selections in NumPy; Processing huge NumPy arrays with memory mapping; Manipulating large arrays with HDF5 and PyTables; Manipulating large heterogeneous tables with HDF5 and PyTables; Chapter 5: High-performance Computing; Introduction; Accelerating pure Python code with Numba and Just-In-Time compilation; Accelerating array computations with Numexpr; Wrapping a C library in Python with ctypes; Accelerating Python code with Cython.
Optimizing Cython code by writing less Python and more CReleasing the GIL to take advantage of ; multi-core processors with Cython and OpenMP; Writing massively parallel code for NVIDIA graphics cards (GPUs) with CUDA; Writing massively parallel code for heterogeneous platforms with OpenCL; Distributing Python code across multiple cores with IPython; Interacting with asynchronous parallel tasks in IPython; Parallelizing code with MPI in IPython; Trying the Julia language in the notebook; Chapter 6: Advanced Visualization; Introduction; Making nicer matplotlib figures with prettyplotlib.

Edition Notes

English.

Classifications

Dewey Decimal Class
006.78
Library of Congress
QA76.73.P98 R6773 2013eb, TK5105.888 .R384 20, QA76.73.P98 R677 2014

The Physical Object

Pagination
1 online resource
Number of pages
512

Edition Identifiers

Open Library
OL38561437M
ISBN 10
178328482X, 1322166226, 1783284811
ISBN 13
9781783284825, 9781322166223, 9781783284818
OCLC/WorldCat
892044237, 1040616819, 894504558

Work Identifiers

Work ID
OL28166916W

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Download catalog record: RDF / JSON / OPDS | Wikipedia citation
August 8, 2024 Edited by MARC Bot import existing book
December 20, 2023 Edited by ImportBot import existing book
December 19, 2022 Edited by MARC Bot import existing book
December 7, 2022 Edited by ImportBot import existing book
June 20, 2022 Created by ImportBot Imported from Internet Archive item record