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BL
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| Record Number
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856097
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| Main Entry
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Rossant, Cyrille.
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| Title & Author
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IPython Interactive Computing and Visualization Cookbook, Second Edition : : Over 100 hands-on recipes to sharpen your skills in high-performance numerical computing and data science in the Jupyter Notebook.
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| Edition Statement
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2nd ed.
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| Publication Statement
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Birmingham :: Packt Publishing,, 2018.
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| Page. NO
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1 online resource (548 pages)
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| ISBN
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1785881930
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: 1785888633
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: 9781785881930
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: 9781785888632
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| Contents
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Cover -- Copyright -- Packt Upshell -- Contributors -- Packt is Searching for Authors Like You -- Table of Contents -- Preface -- Chapter 1: A Tour of Interactive Computing with Jupyter and IPython -- Introduction -- Introducing IPython and the Jupyter Notebook -- Getting started with exploratory data analysis in the Jupyter Notebook -- 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 Jupyter -- Chapter 2: Best Practices in Interactive Computing -- Introduction -- Learning the basics of the Unix shell -- Using the latest features of Python 3 -- Learning the basics of the distributed version control system Git -- A typical workflow with Git branching -- Efficient interactive computing workflows with IPython -- Ten tips for conducting reproducible interactive computing experiments -- Writing high-quality Python code -- Writing unit tests with pytest -- Debugging code with IPython -- Chapter 3: Mastering the Jupyter Notebook -- Introduction -- Teaching programming in the Notebook with IPython Blocks -- Converting a Jupyter notebook to other formats with nbconvert -- Mastering widgets in the Jupyter Notebook -- Creating custom Jupyter Notebook widgets in Python, HTML, and JavaScript -- Configuring the Jupyter Notebook -- Introducing JupyterLab -- Chapter 4: Profiling and Optimization -- Introduction -- Evaluating the time taken by a command 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 NumPy.
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Fitting a Bayesian model by sampling from a posterior distribution with a Markov chain Monte Carlo method -- Analyzing data with the R programming language in the Jupyter Notebook -- Chapter 8: Machine Learning -- Introduction -- Getting started with scikit-learn -- Predicting who will survive on the Titanic with logistic regression -- Learning to recognize handwritten digits with a K-nearest neighbors classifier -- Learning from text -- Naive Bayes for Natural Language Processing -- Using support vector machines for classification tasks -- Using a random forest to select important features for regression -- Reducing the dimensionality of a dataset with a principal component analysis -- Detecting hidden structures in a dataset with clustering -- Chapter 9: Numerical Optimization -- Introduction -- Finding the root of a mathematical function -- Minimizing a mathematical function -- Fitting a function to data with nonlinear least squares -- Finding the equilibrium state of a physical system by minimizing its potential energy -- Chapter 10: Signal Processing -- Introduction -- Analyzing the frequency components of a signal with a Fast Fourier Transform -- Applying a linear filter to a digital signal -- Computing the autocorrelation of a time series -- Chapter 11: Image and Audio Processing -- Introduction -- Manipulating the exposure of an image -- Applying filters on an image -- Segmenting an image -- Finding points of interest in an image -- Detecting faces in an image with OpenCV -- Applying digital filters to speech sounds -- Creating a sound synthesizer in the Notebook -- Chapter 12: Deterministic Dynamical Systems -- Introduction -- Plotting the bifurcation diagram of a chaotic dynamical system -- Simulating an elementary cellular automaton -- Simulating an ordinary differential equation with SciPy.
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Implementing an efficient rolling average algorithm with stride tricks -- Processing large NumPy arrays with memory mapping -- Manipulating large arrays with HDF5 -- Chapter 5: High-Performance Computing -- Introduction -- Using Python to write faster code -- 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 C -- Releasing the GIL to take advantage of -- multi-core processors with Cython and OpenMP -- Writing massively parallel code for NVIDIA graphics cards (GPUs) with CUDA -- Distributing Python code across multiple cores with IPython -- Interacting with asynchronous parallel tasks in IPython -- Performing out-of-core computations on large arrays with Dask -- Trying the Julia programming language in the Jupyter Notebook -- Chapter 6: Data Visualization -- Introduction -- Using Matplotlib styles -- Creating statistical plots easily with seaborn -- Creating interactive web visualizations with Bokeh and HoloViews -- Visualizing a NetworkX graph in the Notebook with D3.js -- Discovering interactive visualization libraries in the Notebook -- Creating plots with Altair and the Vega-Lite specification -- Chapter 7: Statistical Data Analysis -- Introduction -- Exploring a dataset with pandas and Matplotlib -- Getting started with statistical hypothesis testing -- a simple z-test -- Getting started with Bayesian methods -- Estimating the correlation between two variables with a contingency table and a chi-squared test -- Fitting a probability distribution to data with the maximum likelihood method -- Estimating a probability distribution nonparametrically with a kernel density estimation.
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Simulating a partial differential equation -- reaction-diffusion systems and Turing patterns -- Chapter 13: Stochastic Dynamical Systems -- Introduction -- Simulating a discrete-time Markov chain -- Simulating a Poisson process -- Simulating a Brownian motion -- Simulating a stochastic differential equation -- Chapter 14: Graphs, Geometry, and Geographic Information Systems -- Introduction -- Manipulating and visualizing graphs with NetworkX -- Drawing flight routes with NetworkX -- Resolving dependencies in a directed acyclic graph with a topological sort -- Computing connected components in an image -- Computing the Voronoi diagram of a set of points -- Manipulating geospatial data with Cartopy -- Creating a route planner for a road network -- Chapter 15: Symbolic and Numerical Mathematics -- Introduction -- Diving into symbolic computing with SymPy -- Solving equations and inequalities -- Analyzing real-valued functions -- Computing exact probabilities and manipulating random variables -- A bit of number theory with SymPy -- Finding a Boolean propositional formula from a truth table -- Analyzing a nonlinear differential system -- Lotka-Volterra (predator-prey) equations -- Getting started with Sage -- Another Book You May Enjoy -- Index.
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| Abstract
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IPython Interactive Computing and Visualization Cookbook, Second Edition shows you how to analyze and visualize data in the Jupyter Notebook. It will help you become an expert in high-performance computing and visualization for data analysis and scientific modeling.
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| Subject
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Command languages.
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Information visualization.
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Interactive computer systems.
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Python.
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Artificial intelligence.
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Computers-- Data Processing.
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Computers-- Intelligence (AI) Semantics.
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Data capture analysis.
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Information visualization.
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Information visualization.
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Interactive computer systems.
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| Dewey Classification
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005.133
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| LC Classification
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QA76.73.P98.R677 2018
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