Book description
Over 100 hands-on recipes to sharpen your skills in high-performance numerical computing and data science with Python
In Detail
IPython is at the heart of the Python scientific stack. With its widely acclaimed web-based notebook, IPython is today an ideal gateway to data analysis and numerical computing in Python.
IPython Interactive Computing and Visualization Cookbook contains many ready-to-use focused recipes for high-performance scientific computing and data analysis. The first part covers programming techniques, including code quality and reproducibility; code optimization; high-performance computing through dynamic 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.
What You Will Learn
- Code better by writing high-quality, readable, and well-tested programs; profiling and optimizing your code, and conducting reproducible interactive computing experiments
- Master all of the new features of the IPython notebook, including the interactive HTML/JavaScript widgets
- Analyze data with Bayesian and frequentist statistics (Pandas, PyMC, and R), and learn from data with machine learning (scikit-learn)
- Gain valuable insights into signals, images, and sounds with SciPy, scikit-image, and OpenCV
- Learn how to write blazingly fast Python programs with NumPy, PyTables, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA and OpenCL), parallel IPython, MPI, and many more
Table of contents
-
IPython Interactive Computing and Visualization Cookbook
- Table of Contents
- IPython Interactive Computing and Visualization Cookbook
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
- 9. Numerical Optimization
- 10. Signal Processing
- 11. Image and Audio Processing
- 12. Deterministic Dynamical Systems
- 13. Stochastic Dynamical Systems
-
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
-
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
Product information
- Title: IPython Interactive Computing and Visualization Cookbook
- Author(s):
- Release date: September 2014
- Publisher(s): Packt Publishing
- ISBN: 9781783284818
You might also like
book
IPython Interactive Computing and Visualization Cookbook - Second Edition
Learn to use IPython and Jupyter Notebook for your data analysis and visualization work. About This …
book
Python Image Processing Cookbook
Explore Keras, scikit-image, open source computer vision (OpenCV), Matplotlib, and a wide range of other Python …
book
Numerical Computing with Python
Understand, explore, and effectively present data using the powerful data visualization techniques of Python Key Features …
book
Python Data Analysis Cookbook
Over 140 practical recipes to help you make sense of your data with ease and build …
