IPython interactive computing and visualization cookbook
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 compu...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, [England] :
Packt Publishing
2014.
|
| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628705406719 |
Tabla de Contenidos:
- Intro
- 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
- Support files, eBooks, discount offers, and more
- Why Subscribe?
- Free Access for Packt account holders
- Preface
- What this book is
- What this book covers
- Part 1 - Advanced High-Performance Interactive Computing
- Part 2 - Standard Methods in Data Science and Applied Mathematics
- What you need for this book
- Installing Python
- GitHub repositories
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- Downloading the example code
- Downloading the color images
- Errata
- Piracy
- Questions
- 1. A Tour of Interactive Computing with IPython
- Introduction
- What is IPython?
- A brief historical retrospective on Python as a scientific environment
- What's new in IPython 2.0?
- Roadmap for IPython 3.0 and 4.0
- References
- Introducing the IPython notebook
- Getting ready
- How to do it...
- There's more...
- See also
- Getting started with exploratory data analysis in IPython
- How to do it...
- There's more...
- See also
- Introducing the multidimensional array in NumPy for fast array computations
- How to do it...
- How it works...
- There's more...
- See also
- Creating an IPython extension with custom magic commands
- How to do it...
- How it works...
- The InteractiveShell class
- Loading an extension
- There's more...
- See also
- Mastering IPython's configuration system
- How to do it...
- How it works...
- Configurables
- Magics
- There's more...
- See also
- Creating a simple kernel for IPython
- Getting ready
- How to do it...
- How it works...
- There's more...
- 2. Best Practices in Interactive Computing
- Introduction.
- Choosing (or not) between Python 2 and Python 3
- How to do it...
- Main differences in Python 3 compared to Python 2
- Python 2 or Python 3?
- Supporting both Python 2 and Python 3
- Using 2to3
- Writing code that works in Python 2 and Python 3
- There's more...
- See also
- Efficient interactive computing workflows with IPython
- How to do it...
- The IPython terminal
- IPython and text editor
- The IPython notebook
- Integrated Development Environments
- There's more...
- See also
- Learning the basics of the distributed version control system Git
- Getting ready
- How to do it…
- Creating a local repository
- Cloning a remote repository
- How it works…
- There's more…
- See also
- A typical workflow with Git branching
- Getting ready
- How to do it…
- Stashing
- How it works…
- There's more…
- See also
- Ten tips for conducting reproducible interactive computing experiments
- How to do it…
- How it works…
- There's more...
- See also
- Writing high-quality Python code
- How to do it...
- How it works...
- There's more...
- See also
- Writing unit tests with nose
- Getting ready
- How to do it...
- How it works...
- There's more...
- Test coverage
- Workflows with unit testing
- Unit testing and continuous integration
- Debugging your code with IPython
- How to do it...
- The post-mortem mode
- Step-by-step debugging
- There's more...
- GUI debuggers
- 3. Mastering the Notebook
- Introduction
- What is the notebook?
- The notebook ecosystem
- Architecture of the IPython notebook
- Connecting multiple clients to one kernel
- Security in notebooks
- References
- Teaching programming in the notebook with IPython blocks
- Getting ready
- How to do it...
- There's more...
- Converting an IPython notebook to other formats with nbconvert
- Getting ready
- How to do it.
- How it works...
- There's more...
- Adding custom controls in the notebook toolbar
- How to do it...
- There's more...
- See also
- Customizing the CSS style in the notebook
- Getting ready
- How to do it...
- There's more...
- See also
- Using interactive widgets - a piano in the notebook
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Creating a custom JavaScript widget in the notebook - a spreadsheet editor for pandas
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Processing webcam images in real time from the notebook
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- 4. Profiling and Optimization
- Introduction
- Evaluating the time taken by a statement in IPython
- How to do it...
- How it works...
- There's more...
- See also
- Profiling your code easily with cProfile and IPython
- How to do it...
- How it works...
- Premature optimization is the root of all evil
- There's more...
- See also
- Profiling your code line-by-line with line_profiler
- Getting ready
- How do to it...
- How it works...
- There's more...
- Tracing the step-by-step execution of a Python program
- See also
- Profiling the memory usage of your code with memory_profiler
- Getting ready
- How to do it...
- How it works...
- There's more...
- Using memory_profiler for standalone Python programs
- Using the %memit magic command in IPython
- Other tools
- See also
- Understanding the internals of NumPy to avoid unnecessary array copying
- Getting ready
- How to do it...
- How it works...
- Why are NumPy arrays efficient?
- What is the difference between in-place and implicit-copy operations?
- Why can't some arrays be reshaped without a copy?
- What are NumPy broadcasting rules?
- There's more...
- See also.
- Using stride tricks with NumPy
- Getting ready
- How to do it...
- How it works...
- See also
- Implementing an efficient rolling average algorithm with stride tricks
- Getting ready
- How to do it...
- See also
- Making efficient array selections in NumPy
- Getting ready
- How to do it...
- How it works...
- There's more...
- Processing huge NumPy arrays with memory mapping
- How to do it...
- How it works...
- There's more...
- See also
- Manipulating large arrays with HDF5 and PyTables
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- Manipulating large heterogeneous tables with HDF5 and PyTables
- Getting ready
- How to do it...
- How it works...
- There's more...
- See also
- 5. High-performance Computing
- Introduction
- CPython and concurrent programming
- Compiler-related installation instructions
- Linux
- Mac OS X
- Windows
- Python 32-bit
- Python 64-bit
- DLL hell
- References
- Accelerating pure Python code with Numba and just-in-time compilation
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Accelerating array computations with Numexpr
- Getting ready
- How to do it…
- How it works...
- See also
- Wrapping a C library in Python with ctypes
- Getting ready
- How to do it…
- How it works…
- There's more…
- Accelerating Python code with Cython
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Optimizing Cython code by writing less Python and more C
- How to do it…
- How it works…
- There's more…
- See also
- Releasing the GIL to take advantage of multicore processors with Cython and OpenMP
- Getting ready
- How to do it…
- How it works…
- See also
- Writing massively parallel code for NVIDIA graphics cards (GPUs) with CUDA
- Getting ready
- How to do it...
- How it works….
- There's more…
- See also
- Writing massively parallel code for heterogeneous platforms with OpenCL
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Distributing Python code across multiple cores with IPython
- How to do it…
- How it works…
- There's more…
- Dependent parallel tasks
- Alternative parallel computing solutions
- References
- See also
- Interacting with asynchronous parallel tasks in IPython
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Parallelizing code with MPI in IPython
- Getting ready
- How to do it…
- How it works…
- See also
- Trying the Julia language in the notebook
- Getting ready
- How to do it…
- How it works…
- There's more…
- 6. Advanced Visualization
- Introduction
- Making nicer matplotlib figures with prettyplotlib
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Creating beautiful statistical plots with seaborn
- Getting ready
- How to do it…
- There's more…
- See also
- Creating interactive web visualizations with Bokeh
- Getting ready
- How to do it…
- There's more…
- See also
- Visualizing a NetworkX graph in the IPython notebook with D3.js
- Getting ready
- How to do it…
- There's more…
- See also
- Converting matplotlib figures to D3.js visualizations with mpld3
- Getting ready
- How to do it…
- How it works…
- There's more…
- See also
- Getting started with Vispy for high-performance interactive data visualizations
- Getting ready
- How to do it…
- How it works…
- There's more…
- Vispy for scientific visualization
- 7. Statistical Data Analysis
- Introduction
- What is statistical data analysis?
- A bit of vocabulary
- Exploration, inference, decision, and prediction
- Univariate and multivariate methods
- Frequentist and Bayesian methods.
- Parametric and nonparametric inference methods.
