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Numerical Python: scientific computing and data science applications with Numpy, SciPy and Matplotlib

By: Johansson, Robert.
Material type: materialTypeLabelBookPublisher: New York Apress Media 2019Edition: 2nd.Description: xxiii, 700 p. Includes appendix and index.ISBN: 9781484246481.Subject(s): Python - Computer program language | Big data | Artificial intelligence | Computer software | Mathematical analysis | Data mining | Application software - DevelopmentDDC classification: 005.133 Summary: Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis. After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. What You'll Learn 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 scikit-learn Optimize Python code using Numba and Cython Who This Book Is For Developers who want to understand how to use Python and its related ecosystem for numerical computing. https://www.apress.com/gp/book/9781484242452
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Books Vikram Sarabhai Library
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Slot 67 (0 Floor, West Wing) Non-fiction 005.133 J6N8 (Browse shelf) Available 201119

Table of Contents

Introduction to computing with Python
Vectors, matrices, and multidimensional arrays
Symbolic computing
Plotting and visualization
Equation solving
Optimization
Interpolation
Integration
Ordinary differential equations
Sparse matrices and graphs
Partial differential equations
Data processing and analysis
Statistics
Statistical modeling
Machine learning
Bayesian statistics
Signal processing
Data input and output
Code optimization
Appendix: Installation.

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more.
Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis.
After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning.
What You'll Learn
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 scikit-learn
Optimize Python code using Numba and Cython
Who This Book Is For
Developers who want to understand how to use Python and its related ecosystem for numerical computing.

https://www.apress.com/gp/book/9781484242452

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