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. Detecting faces in an image with OpenCV, 11.6. Creating a sound synthesizer in the Notebook, Chapter 12 : Deterministic Dynamical Systems, 12.1. Computing the Voronoi diagram of a set of points, 14.6. Get Jupyter Cookbook now with O’Reilly online learning. The client can be a Qt widget if we run the Qt console, or a browser if we run the Jupyter Notebook. Introduction. Mastering widgets in the Jupyter Notebook, 3.4. Applying a linear filter to a digital signal, 10.3. Simulating a stochastic differential equation, Chapter 14 : Graphs, Geometry, and Geographic Information Systems, 14.1. Processing large NumPy arrays with memory mapping, 5.2. 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. Mastering widgets in the Jupyter Notebook; 3.4. You have probably heard of, and maybe used, the Jupyter Notebook. Découvrez des commentaires utiles de client et des classements de commentaires pour Jupyter Cookbook: Over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more sur Amazon.fr. This signature is generated using a secret key owned by every user. Teaching programming in the Notebook with IPython blocks, 3.2. Simulating a discrete-time Markov chain, 13.4. The kernel executes the code and returns the result to the client for display. In fact, this very book is entirely written in the Notebook using the Markdown format and a custom-made Python tool. Contents ; Bookmarks Installation and Setting up the Environment . have moved to new projects under the name Jupyter. Notebooks are being used to create slides, teaching materials, blog posts, research papers, and even books. – cel Mar 21 '15 at 5:51. Either the miniconda or the miniforge conda distributions include a minimal conda installation. Most of the book is freely available on this website ( CC-BY-NC-ND license ). The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics, © Cyrille Rossant – What is Python? IPython Interactive Computing and Visualization Cookbook, Second Edition (2018), by Cyrille Rossant, contains over 100 hands-on recipes on high-performance numerical computing and data science in the Jupyter Notebook. Computing the autocorrelation of a time series, 11.1. Real-time Data Processing and Analytics. Fitting a function to data with nonlinear least squares, 9.4. Implementing an efficient rolling average algorithm with stride tricks, 4.8. 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. Creating an IPython extension with custom magic commands, 1.5. 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. The security model is based on a cryptographic signature present in every notebook. Programming / DevOps. Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Code of the IPython Cookbook, Second Edition (2018) This repository contains the Jupyter notebooks of the 100+ recipes of IPython Interactive Computing and Visualization Cookbook, Second Edition (2018), by Cyrille Rossant, Packt Publishing. Using the latest features of Python 3, 2.3. Installing Jupyter on Windows. Exploring a dataset with pandas and matplotlib, 7.2. For this reason, Jupyter has a security model where HTML and JavaScript code in a notebook can be either trusted or untrusted. Analyzing data with the R programming language in the Jupyter Notebook, 8.2. Data Cloud. testing course. Teaching programming in the Notebook with IPython blocks, 3.2. In this chapter, we will cover the following topics: In this chapter, we will see several advanced features and usage examples of the Jupyter Notebook. Adding an Engine. Learning the basics of the Unix shell, 2.2. It is still in an early stage of development at the time of this writing. Introducing JupyterLab. Computing exact probabilities and manipulating random variables, 15.6. Creating custom Jupyter Notebook widgets in Python, HTML, and JavaScript; 3.5. Creating plots with Altair and the Vega-Lite specification, 7.1. All 100+ recipes have been updated to the latest versions of Python, IPython, Jupyter, and all of the scientific packages. ▶ Buy the book. Converting a Jupyter notebook to other formats with nbconvert, 3.3. Jupyter’s Notebooks and dashboard are web apps, and Jupyter starts up a local Python server to serve these apps to your web browser, making it essentially platform-independent and opening the door to easier sharing on the web. Adding the R engine. Machine Learning Tools . I've created multiple python modules as .py files in a Python IDE called Pyzo in the following path: 'C:\Users\Michael\Anaconda3\Lib\site-packages' which I can then import like regular Python packages such as pandas and numpy into my Jupyter notebook or into Pyzo. Manipulating the exposure of an image, 11.4. Profiling your code easily with cProfile and IPython, 4.3. IPython Cookbook, Second Edition (2018) IPython Interactive Computing and Visualization Cookbook, Second Edition (2018), by Cyrille Rossant, contains over 100 hands-on recipes on high-performance numerical computing and data science in the Jupyter Notebook. Accelerating pure Python code with Numba and just-in-time compilation, 5.3. Contents ; Bookmarks Installation and Setting up the Environment. Built with Pure Theme Jupyter, as a project, grew out of Python, so it is somewhat dependent on which version of Python you have installed. Predicting who will survive on the Titanic with logistic regression, 8.3. Text on GitHub with a CC-BY-NC-ND license Code on GitHub with a MIT license We cover it in the last recipe of this chapter. Achetez et téléchargez ebook Jupyter Cookbook: Over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more (English Edition): Boutique Kindle - Modeling & Simulation : Amazon.fr Start your free trial . Jupyter Cookbook. Distributing Python code across multiple cores with IPython, 5.10. The client is the interface offering the user the ability to send code to the kernel. Optimizing Cython code by writing less Python and more C, 5.7. Finding points of interest in an image, 11.5. JSON is a language-independent, text-based file format for representing structured documents. There is an ecosystem of tools around the Notebook. This book contains intuitive recipes on building interactive widgets to … In the Jupyter Notebook, the kernel receives entire cells at once, so it has no notion of a notebook. A typical workflow with Git branching, 2.5. Simulating an ordinary differential equation with SciPy, 12.4. 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. Profiling the memory usage of your code with memory_profiler, 4.5. Discovering interactive visualization libraries in the Notebook, 6.6. Introduction. Outputs generated by the user are always trusted. Getting started with Bayesian methods, 7.4. In this introduction, we will give a broad overview of Python, IPython, Jupyter, and the scientific Python ecosystem. To make it work, I installed latex, typical latex extra, and pandoc. Installing Jupyter on Windows. Creating interactive Web visualizations with Bokeh and HoloViews, 6.4. Des milliers de livres avec la livraison chez vous en 1 jour ou en magasin avec -5% de réduction . Ils sont manipulables interactivement dans un navigateur web. Design Dynamics. This repository is … O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers. 18 Jupyter et ses notebooks. Reducing the dimensionality of a dataset with a principal component analysis, 8.8. Getting started with exploratory data analysis in the Jupyter Notebook, 1.3. Manipulating geospatial data with Cartopy, 14.7. JupyterLab: Jupyter’s Next-Generation Notebook Interface JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data. Fitting a Bayesian model by sampling from a posterior distribution with a Markov Chain Monte Carlo method, 7.8. JupyterLab is the next generation of the Jupyter Notebook. Visualizing a NetworkX graph in the Notebook with D3.js, 6.5. Among many other features, Juptyter provides a framework of extensible commands called magics (actually, this only works with the IPython kernel of Jupyter, but that is the one we are concerned with), which allow you to extend the language in many useful ways. Installing Jupyter on the Mac. In the Read-Evaluate-Print Loop (REPL) terminology, the kernel implements the Evaluate, whereas the client implements the Read and the Print of the process. ▶ Get the code as Jupyter notebooks Diving into symbolic computing with SymPy, 15.4. Retrouvez Jupyter Cookbook: Over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more et des millions de livres en … Route planner for a road network, Chapter 13: Stochastic Dynamical Systems, 12.1 formula from posterior. Distributing Python code across multiple cores with IPython blocks, 3.2 send code to the.... And a client and the underlying kernel using WebSocket, a TCP-based protocol implemented in modern web.. 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