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Learn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, Matplotlib, and more. This book demonstrates how to work with mathematical modeling and solve problems with numerical, symbolic, and visualization techniques. It explores applications in science, engineering, data analytics, and more.
Numerical Python, Third Edition, presents many case study examples of applications in fundamental scientific computing disciplines, as well as in Data Science and statistics. This fully revised edition, updated for each library's latest version, demonstrates Python's power for rapid development and exploratory computing due to its simple and high-level syntax and many powerful libraries and tools for computation and 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.
Computing is an interdisciplinary activity that requires experience and expertise in both theoretical and practical subjects: a firm understanding of mathematics and scientific thinking is a fundamental requirement for effective computational work. Equally important is solid training in computer programming and Computer Science. The role of this book is to bridge these two subjects by introducing how scientific computing can be done using the Python programming language and the computing environment that has appeared around this language. In this book, the reader is assumed to have some previous training in mathematics and numerical methods and basic knowledge of Python programming. The book’s focus is to give a practical introduction to computational problem-solving with Python. Brief introductions to the theory of the covered topics are provided in each chapter to introduce notation and remind readers of the basic methods and algorithms. However, this book is not a self-consistent treatment of numerical methods. To assist readers who have yet to become familiar with some of the topics of this book, references for further reading are given at the end of each chapter. Likewise, readers without experience in Python programming will find it helpful to read this book with a book that focuses on the Python programming language itself.
What You'll Learn:
Work with vectors and matrices using NumPy
Review Symbolic computing with SymPy
Plot and visualize data with Matplotlib
Perform data analysis tasks with Pandas and SciPy
Understand statistical modeling and machine learning with statsmodels and scikit-learn
Optimize Python code using Numba and Cython
Spyder: An Integrated Development Environment
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