文件名称:
Numerical.Python.A.Practical.Techniques.Approach.for.Industry.1484205545
开发工具:
文件大小: 12mb
下载次数: 0
上传时间: 2015-10-12
详细说明： Leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, SciPy, SymPy, Matplotlib, Pandas, and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more. After reading and using Numerical Python, you will have seen examples and case studies from many areas of computing, and gained familiarity with basic computing techniques such as array-based and symbolic computing, all-around practical skills such as visualisation and numerical file I/O, general computational methods such as equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. Python has gained widespread popularity as a computing language: It is nowadays employed for computing by practitioners in such diverse fields as for example scientific research, engineering, finance, and data analytics. One reason for the popularity of Python is its high-level and easy-to-work-with syntax, which enables the rapid development and exploratory computing that is required in modern computational work. What you’ll learn How to work with vectors and matrices using NumPy How to work with symbolic computing using SymPy How to plot and visualize data with Matplotlib How to solve linear and nonlinear equations with SymPy and SciPy How to solve solve optimization, interpolation, and integration problems using SciPy How to solve ordinary and partial differential equations with SciPy and FEniCS How to perform data analysis tasks and solve statistical problems with Pandas and SciPy How to work with statistical modeling and machine learning with statsmodels and scikit-learn How to handle file I/O using HDF5 and other common file formats for numerical data How to optimize Python code using Numba and Cython Who this book is for This practical book is for those practicing industry coders, data scientists, engineers, financial engineers, scientists, business managers and more who use or plan to use numerical Python techniques and methods. Table of Contents Chapter 1. Introduction to computing with Python Chapter 2. Vectors, matrices and multidimensional arrays Chapter 3. Symbolic computing Chapter 4. Plotting and visualization Chapter 5. Equation solving Chapter 6. Optimization Chapter 7. Interpolation Chapter 8. Integration Chapter 9. Ordinary differential equations Chapter 10. Sparse matrices and graphs Chapter 11. Partial differential equations Chapter 12. Data processing and analysis Chapter 13. Statistics Chapter 14. Statistical modeling Chapter 15. Machine learning Chapter 16. Bayesian statistics Chapter 17. Signal processing Chapter 18. Data input and output Chapter 19. Code optimization Appendix: Installation ...展开收缩
(系统自动生成,下载前可以参看下载内容)
下载文件列表
相关说明
- 本站资源为会员上传分享交流与学习,如有侵犯您的权益,请联系我们删除.
- 本站是交换下载平台，提供交流渠道，下载内容来自于网络，除下载问题外，其它问题请自行百度。
- 本站已设置防盗链，请勿用迅雷、QQ旋风等多线程下载软件下载资源，下载后用WinRAR最新版进行解压.
- 如果您发现内容无法下载，请稍后再次尝试；或者到消费记录里找到下载记录反馈给我们.
- 下载后发现下载的内容跟说明不相乎，请到消费记录里找到下载记录反馈给我们，经确认后退回积分.
- 如下载前有疑问，可以通过点击"提供者"的名字，查看对方的联系方式，联系对方咨询.
