Numerical Python Scientific Computing and Data Science Applications, 2nd Edition

Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib By 作者: Robert Johansson ISBN10 书号: 1484242459 ISBN13 书号: 9781484242452 Edition 版本: 2nd ed. 出版日期: 20181225 pages 页数: (700 ) Work with vectors and matrices using NumPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Review statistical modeling and machine learning with statsmodels and scikitlearn Optimize Python code using Numba and Cython Cover Front Matter 1. Introduction to Computing with Python 2. Vectors, Matrices, and Multidimensional Arrays 3. Symbolic Computing 4. Plotting and Visualization 5. Equation Solving 6. Optimization 7. Interpolation 8. Integration 9. Ordinary Differential Equations 10. Sparse Matrices and Graphs 11. Partial Differential Equations 12. Data Processing and Analysis 13. Statistics 14. Statistical Modeling 15. Machine Learning 16. Bayesian Statistics 17. Sinal Processing 18. Data Input and Output 19. Code Optimization

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Learning SciPy for Numerical and Scientific Computing, 2nd Edition
20180705Learning SciPy for Numerical and Scientific Computing, 第二版
 3.28MB
Learning_SciPy_for_Numerical_and_Scientific_Computing.pdf.pdf
20190914Learning_SciPy_for_Numerical_and_Scientific_Computing.pdf
 38.89MB
Numerical Python, 2nd Edition epub
20181227Numerical Python, 2nd Edition. Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib。Apress。 英文版。 epub格式，不是pdf。 作者：Robert Johansson
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Apress  Numerical Python, 2nd.2019.epub
20190103Apress  Numerical Python, Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
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Mastering Python Scientific Computing
20160519Mastering Python Scientific Computing By Hemant Kumar Mehta 2015  300 Pages A complete guide for Python programmers to master scientific computing using Python APIs and tools If you are a Python programmer and want to get your hands on scientific computing, this book is for you. The book expects you to have had exposure to various concepts of Python programming. In today's world, along with theoretical and experimental work, scientific computing has become an important part of scientific disciplines. Numerical calculations, simulations and computer modeling in this day and age form the vast majority of both experimental and theoretical papers. In the scientific method, replication and reproducibility are two important contributing factors. A complete and concrete scientific result should be reproducible and replicable. Python is suitable for scientific computing. A large community of users, plenty of help and documentation, a large collection of scientific libraries and environments, great performance, and good support makes Python a great choice for scientific computing. At present Python is among the top choices for developing scientific workflow and the book targets existing Python developers to master this domain using Python. The main things to learn in the book are the concept of scientific workflow, managing scientific workflow data and performing computation on this data using Python.
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Introduction to Scientific Computing and Data Analysis(Springer,2015)
20160823This textbook provides and introduction to numerical computing and its applications in science and engineering. The topics covered include those usually found in an introductory course, as well as those that arise in data analysis. This includes optimization and regression based methods using a singular value decomposition. The emphasis is on problem solving, and there are numerous exercises throughout the text concerning applications in engineering and science. The essential role of the mathematical theory underlying the methods is also considered, both for understanding how the method works, as well as how the error in the computation depends on the method being used. The MATLAB codes used to produce most of the figures and data tables in the text are available on the author’s website and SpringerLink.
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Scientific and Engineering Applications Using MATLAB 2nd Edition
20180405The purpose of this book is to present scientific and engineering works whose numerical and graphical analysis were all constructed using the power of MATLABÂ® tools. This book is a collection of interesting examples of where this computational package can be applied. The first five chapters of this book show applications in seismology, meteorology and natural environment. Chapters 6 and 7 focus on modeling and simulation of Water Distribution Networks. Simulation was also applied to study wide area protection for interconnected power grids (Chapter 8) and performance of conical antennas (Chapter 9). The last chapter deals with depth positioning of underwater robot vehicles. Contents 1 Ground Motion Estimation During Strong Seismic Events Using Matlab 2 Aftershock Identification Through Genetic FaultPlane Fitting 3 Sea Surface Temperature (SST) and the Indian Summer Monsoon 4 The Analysis of Influence of River Floods on Biotic Components of Floodplain Ecosystems with the Help of MATLAB Simulation 5 Data Reduction for Water Quality Modelling, Vaal Basin 6 Modelling Reliability Based Optimization Design for Water Distribution Networks 7 Integrated CyberPhysical Simulation of Intelligent Water Distribution Networks 8 A Novel Wide Area Protection Classification Technique for Interconnected Power Grids Based on MATLAB Simulation 9 Simulated Performance of Conical Antennas Using MatlabBased FiniteDifference Time Domain (FDTD) Code 10 Variable Ballast Mechanism for Depth Positioning of a Spherical Underwater Robot Vehicle
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Learning SciPy for Numerical and Scientific Computing(2nd) 无水印pdf
20171003Learning SciPy for Numerical and Scientific Computing(2nd) 英文无水印pdf 第2版 pdf所有页面使用FoxitReader和PDFXChangeViewer测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除
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Python for Data Analysis 2nd by McKinney 原版pdf
20180411Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this handson guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. •Use the IPython shell and Jupyter notebook for exploratory computing •Learn basic and advanced features in NumPy (Numerical Python) •Get started with data analysis tools in the pandas library •Use flexible tools to load, clean, transform, merge, and reshape data •Create informative visualizations with matplotlib •Apply the pandas groupby facility to slice, dice, and summarize datasets •Analyze and manipulate regular and irregular time series data •Learn how to solve realworld data analysis problems with thorough, detailed examples
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Learning SciPy for Numerical and Scientific Computing(PACKT,2ed,2015)
20150410It's essential to incorporate workflow data and code from various sources in order to create fast and effective algorithms to solve complex problems in science and engineering. Data is coming at us faster, dirtier, and at an ever increasing rate. There is no need to employ difficulttomaintain code, or expensive mathematical engines to solve your numerical computations anymore. SciPy guarantees fast, accurate, and easytocode solutions to your numerical and scientific computing applications. Learning SciPy for Numerical and Scientific Computing unveils secrets to some of the most critical mathematical and scientific computing problems and will play an instrumental role in supporting your research. The book will teach you how to quickly and efficiently use different modules and routines from the SciPy library to cover the vast scope of numerical mathematics with its simplistic practical approach that's easy to follow.
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IPython Interactive Computing and Visualization Cookbook, 2ndPackt2018.pdf
20180326We are becoming awash in the flood of digital data from scientific research, engineering, economics, politics, journalism, business, and many other domains. As a result, analyzing, visualizing, and harnessing data is the occupation of an increasingly large and diverse set of people. Quantitative skills such as programming, numerical computing, mathematics, statistics, and data mining, which form the core of data science, are more and more appreciated in a seemingly endless plethora of fields. Python, a widelyknown programming language, is also one of the leading open platforms for data science. IPython is a mature Python project that provides scientistfriendly interactive access to Python. It is part of the broader Project Jupyter, which aims to provide highquality environments for interactive computing, data analysis, visualization, and the authoring of interactive scientific documents. Jupyter is estimated to have several million users today. The prequel of this book, Learning IPython for Interactive Computing and Data Visualization Second Edition, Packt Publishing was published in 2015, two years after the first edition. It is a beginnerlevel introduction to data science and numerical computing with Python, IPython, and Jupyter. This book, the first edition of which was published in 2014, continues that journey by presenting more than 100 recipes for interactive scientific computing and data science. These recipes not only cover programming topics such as numerical computing, highperformance computing, parallel computing, and interactive visualization, but also data analysis topics such as statistics, data mining, machine learning, signal processing, graph theory, numerical optimization, and many others. This second edition is fully compatible with the latest versions of the platform and its libraries. It includes new recipes to better leverage the latest features of Python 3, and it introduces promising new projects such as JupyterLab, Altair, and Dask.
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用python进行数据分析 第二版 Python for Data Analysis, 2nd Edition
20180406用python进行数据分析 第二版 英文高清带书签版本 Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this handson guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve realworld data analysis problems with thorough, detailed examples
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IPython Interactive Computing and Visualization Cookbook(PACKT,2014)
20150908IPython is at the heart of the Python scientific stack. With its widely acclaimed webbased notebook, IPython is today an ideal gateway to data analysis and numerical computing in Python. IPython Interactive Computing and Visualization Cookbook contains many readytouse focused recipes for highperformance scientific computing and data analysis. The first part covers programming techniques, including code quality and reproducibility; code optimization; highperformance 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.
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Numerical Python 无水印pdf
19540215Numerical Python 英文无水印pdf pdf所有页面使用FoxitReader和PDFXChangeViewer测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除
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