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IPython Interactive Computing and Visualization Cookbook
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Intendedtoanyoneinterestedinnumericalcomputinganddatascience:students,researchers,teachers,engineers,analysts,hobbyists..BasicknowledgeofPython/NumPyisrecommended.Someskillsinmathematicswillhelpyouunderstandthetheorybehindthecomputationalmethods.
目錄(149章)
倒序
- 封面
- 版權(quán)頁
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Support files eBooks discount offers and more
- Preface
- What this book is
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- 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 3
- Efficient 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 notebook
- Using 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 NumPy
- Implementing 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 C
- Releasing the GIL to take advantage of multicore 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
- Creating beautiful statistical plots with seaborn
- Creating interactive web visualizations with Bokeh
- Visualizing a NetworkX graph in the IPython notebook with D3.js
- Converting matplotlib figures to D3.js visualizations with mpld3
- Getting started with Vispy for high-performance interactive data visualizations
- 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 IPython 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
- Analyzing a social network 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 Shapely and basemap
- 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(shí)間：2021-08-05 17:57:54
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