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IPython Interactive Computing and Visualization Cookbook（Second Edition）
Cyrille Rossant 著
更新時間：2021-07-02 16:24:14
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最新章節：
Index
Thisbookisintendedforanyoneinterestedinnumericalcomputinganddatascience:students,researchers,teachers,engineers,analysts,andhobbyists.AbasicknowledgeofPython/NumPyisrecommended.Someskillsinmathematicswillhelpyouunderstandthetheorybehindthecomputationalmethods.
最新章節
- Index
- Getting started with Sage
- Analyzing a nonlinear differential system — Lotka-Volterra (predator-prey) equations
- Finding a Boolean propositional formula from a truth table
- A bit of number theory with SymPy
- Computing exact probabilities and manipulating random variables
品牌：中圖公司
上架時間：2021-07-02 16:20:48
出版社：Packt Publishing
本書數字版權由中圖公司提供，并由其授權上海閱文信息技術有限公司制作發行
- Index 更新時間：2021-07-02 16:24:14
- Getting started with Sage
- Analyzing a nonlinear differential system — Lotka-Volterra (predator-prey) equations
- Finding a Boolean propositional formula from a truth table
- A bit of number theory with SymPy
- Computing exact probabilities and manipulating random variables
- Analyzing real-valued functions
- Solving equations and inequalities
- Diving into symbolic computing with SymPy
- Introduction
- Chapter 15. Symbolic and Numerical Mathematics
- Creating a route planner for a road network
- Manipulating geospatial data with Cartopy
- Computing the Voronoi diagram of a set of points
- Computing connected components in an image
- Resolving dependencies in a directed acyclic graph with a topological sort
- Drawing flight routes with NetworkX
- Manipulating and visualizing graphs with NetworkX
- Introduction
- Chapter 14. Graphs Geometry and Geographic Information Systems
- Simulating a stochastic differential equation
- Simulating a Brownian motion
- Simulating a Poisson process
- Simulating a discrete-time Markov chain
- Introduction
- Chapter 13. Stochastic Dynamical Systems
- Simulating a partial differential equation — reaction-diffusion systems and Turing patterns
- Simulating an ordinary differential equation with SciPy
- Simulating an elementary cellular automaton
- Plotting the bifurcation diagram of a chaotic dynamical system
- Introduction
- Chapter 12. Deterministic Dynamical Systems
- Creating a sound synthesizer in the Notebook
- Applying digital filters to speech sounds
- Detecting faces in an image with OpenCV
- Finding points of interest in an image
- Segmenting an image
- Applying filters on an image
- Manipulating the exposure of an image
- Introduction
- Chapter 11. Image and Audio Processing
- Computing the autocorrelation of a time series
- Applying a linear filter to a digital signal
- Analyzing the frequency components of a signal with a Fast Fourier Transform
- Introduction
- Chapter 10. Signal Processing
- Finding the equilibrium state of a physical system by minimizing its potential energy
- Fitting a function to data with nonlinear least squares
- Minimizing a mathematical function
- Finding the root of a mathematical function
- Introduction
- Chapter 9. Numerical Optimization
- Detecting hidden structures in a dataset with clustering
- Reducing the dimensionality of a dataset with a principal component analysis
- Using a random forest to select important features for regression
- Using support vector machines for classification tasks
- Learning from text – Naive Bayes for Natural Language Processing
- Learning to recognize handwritten digits with a K-nearest neighbors classifier
- Predicting who will survive on the Titanic with logistic regression
- Getting started with scikit-learn
- Introduction
- Chapter 8. Machine Learning
- Analyzing data with the R programming language in the Jupyter Notebook
- Fitting a Bayesian model by sampling from a posterior distribution with a Markov chain Monte Carlo method
- Estimating a probability distribution nonparametrically with a kernel density estimation
- Fitting a probability distribution to data with the maximum likelihood method
- Estimating the correlation between two variables with a contingency table and a chi-squared test
- Getting started with Bayesian methods
- Getting started with statistical hypothesis testing — a simple z-test
- Exploring a dataset with pandas and Matplotlib
- Introduction
- Chapter 7. Statistical Data Analysis
- Creating plots with Altair and the Vega-Lite specification
- Discovering interactive visualization libraries in the Notebook
- Visualizing a NetworkX graph in the Notebook with D3.js
- Creating interactive web visualizations with Bokeh and HoloViews
- Creating statistical plots easily with seaborn
- Using Matplotlib styles
- Introduction
- Chapter 6. Data Visualization
- Trying the Julia programming language in the Jupyter Notebook
- Performing out-of-core computations on large arrays with Dask
- Interacting with asynchronous parallel tasks in IPython
- Distributing Python code across multiple cores with IPython
- Writing massively parallel code for NVIDIA graphics cards (GPUs) with CUDA
- Releasing the GIL to take advantage of multi-core processors with Cython and OpenMP
- Optimizing Cython code by writing less Python and more C
- Accelerating Python code with Cython
- Wrapping a C library in Python with ctypes
- Accelerating array computations with NumExpr
- Accelerating pure Python code with Numba and Just-In-Time compilation
- Using Python to write faster code
- Introduction
- Chapter 5. High-Performance Computing
- Manipulating large arrays with HDF5
- Processing large NumPy arrays with memory mapping
- Implementing an efficient rolling average algorithm with stride tricks
- Using stride tricks with NumPy
- Understanding the internals of NumPy to avoid unnecessary array copying
- Profiling the memory usage of your code with memory_profiler
- Profiling your code line-by-line with line_profiler
- Profiling your code easily with cProfile and IPython
- Evaluating the time taken by a command in IPython
- Introduction
- Chapter 4. Profiling and Optimization
- Introducing JupyterLab
- Configuring the Jupyter Notebook
- Creating custom Jupyter Notebook widgets in Python HTML and JavaScript
- Mastering widgets in the Jupyter Notebook
- Converting a Jupyter notebook to other formats with nbconvert
- Teaching programming in the Notebook with IPython Blocks
- Introduction
- Chapter 3. Mastering the Jupyter Notebook
- Debugging code with IPython
- Writing unit tests with pytest
- Writing high-quality Python code
- Ten tips for conducting reproducible interactive computing experiments
- Efficient interactive computing workflows with IPython
- A typical workflow with Git branching
- Learning the basics of the distributed version control system Git
- Using the latest features of Python 3
- Learning the basics of the Unix shell
- Introduction
- Chapter 2. Best Practices in Interactive Computing
- Creating a simple kernel for Jupyter
- Mastering IPython's configuration system
- Creating an IPython extension with custom magic commands
- Introducing the multidimensional array in NumPy for fast array computations
- Getting started with exploratory data analysis in the Jupyter Notebook
- Introducing IPython and the Jupyter Notebook
- Introduction
- Chapter 1. A Tour of Interactive Computing with Jupyter and IPython
- Get in touch
- Sections
- To get the most out of this book
- What this book covers
- Who this book is for
- Preface
- Packt is Searching for Authors Like You
- About the author
- Contributors
- PacktPub.com
- Why subscribe?
- IPython Interactive Computing and Visualization CookbookSecond Edition
- coverpage
- coverpage
- IPython Interactive Computing and Visualization CookbookSecond Edition
- Why subscribe?
- PacktPub.com
- Contributors
- About the author
- Packt is Searching for Authors Like You
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Sections
- Get in touch
- 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
- 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
- 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
- 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
- Index 更新時間：2021-07-02 16:24:14
