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IPython Interactive Computing and Visualization Cookbook
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 , Second Edition

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IPython Interactive Computing and Visualization Cookbook

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Key benefits

  • • Leverage the Jupyter Notebook for interactive data science 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

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.

Who is this book 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.

What you will learn

  • • Master all features of the Jupyter Notebook
  • • Code better: write high-quality, readable, and well-tested programs; profile and optimize your code; and conduct reproducible interactive computing experiments
  • • Visualize data and create interactive plots in the Jupyter Notebook
  • • Write blazingly fast Python programs with NumPy, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA), parallel IPython, Dask, and more
  • • Analyze 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 OpenCV
  • • Simulate deterministic and stochastic dynamical systems in Python
  • • Familiarize yourself with math in Python using SymPy and Sage: algebra, analysis, logic, graphs, geometry, and probability theory

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 31, 2018
Length: 548 pages
Edition : 2nd
Language : English
ISBN-13 : 9781785881930
Category :
Languages :
Concepts :
Tools :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
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Product Details

Publication date : Jan 31, 2018
Length: 548 pages
Edition : 2nd
Language : English
ISBN-13 : 9781785881930
Category :
Languages :
Concepts :
Tools :

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Table of Contents

16 Chapters
1. A Tour of Interactive Computing with Jupyter and IPython Chevron down icon Chevron up icon
2. Best Practices in Interactive Computing Chevron down icon Chevron up icon
3. Mastering the Jupyter Notebook Chevron down icon Chevron up icon
4. Profiling and Optimization Chevron down icon Chevron up icon
5. High-Performance Computing Chevron down icon Chevron up icon
6. Data Visualization Chevron down icon Chevron up icon
7. Statistical Data Analysis Chevron down icon Chevron up icon
8. Machine Learning Chevron down icon Chevron up icon
9. Numerical Optimization Chevron down icon Chevron up icon
10. Signal Processing Chevron down icon Chevron up icon
11. Image and Audio Processing Chevron down icon Chevron up icon
12. Deterministic Dynamical Systems Chevron down icon Chevron up icon
13. Stochastic Dynamical Systems Chevron down icon Chevron up icon
14. Graphs, Geometry, and Geographic Information Systems Chevron down icon Chevron up icon
15. Symbolic and Numerical Mathematics Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
(7 Ratings)
5 star 42.9%
4 star 57.1%
3 star 0%
2 star 0%
1 star 0%
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gary d. smith Jul 28, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent for my needs as a beginner with Jupyter. very well written. definitely recommend.
Amazon Verified review Amazon
User0910 May 13, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Le livre va bien au-delà d'IPython et semble en fait s'adresser au scientifique ayant déjà une pratique de python en lui proposant de mettre à jour ses connaissances sur l'écosystème de Python gravitant en gros autour du calcul numérique.Dans l'absolu, le livre est un peu touche à tout, balayant un nombre impressionnant de librairies en 500 pages, là où une seule pourrait faire l'objet d'un livre séparé. D'ordinaire, ce genre d'inventaire à la Prévert a le don d'agacer pour qui n'aime pas le survol.Or ici, un miracle se produit. Peut-être suis-je exactement le lecteur ciblé par l'auteur, mais quasiment chaque section de chaque chapitre fait mouche. Bien qu'utilisant python régulièrement pour mon travail, je m'aperçois à quel point je reste prisonnier des outils que je connais, et combien je suis ignorant de l'incroyable richesse de l'écosystème scientifique et computationel de python.Je n'ai tout simplement jamais mis la main sur un livre contenant une telle densité informative. Bien sûr, aucun sujet n'est réellement approfondi, mais le plus souvent, les librairies ne sont pas simplement mentionnées, mais utilisées dans un exemple informatif qui met le pied à l'étrier.Chapeau à l'auteur pour ses connaissances et sa maîtrise qui transpirent à chaque page de ce livre impressionnant.
Amazon Verified review Amazon
Carol Willing Feb 19, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The "Cookbook" update adds new content about Jupyter and Python 3.6. It provides a comprehensive, yet understandable, reference to Jupyter, IPython, and frequently used Python data science libraries. The code examples clearly demonstrate how to use popular Python libraries with IPython and Jupyter. Each section contains links to additional resources and provides the reader a wealth of high-quality tips and use cases.I highly recommend the "Cookbook" to people getting started with IPython/Jupyter. The examples and content will also delight current users of IPython/Jupyter. An industry standard, the "Cookbook", is now even better with additional content, updates, and color available for visualizations.
Amazon Verified review Amazon
David E. Jun 04, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Very informative cookbook on IPython.
Amazon Verified review Amazon
Amazon Customer May 11, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
very happy with my book what more can you say !
Amazon Verified review Amazon
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