Numerical Python. Scientific computing and data science applications with Numpy, SciPy and Matplotlib

Article number 10125660

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, FEniCS, Matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates computation problem solving and techniques that have applications in such diverse fields as scientific research, engineering, finance, and data analytics. Numerical Python, Second Edition, presents many 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. You Will: 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 Related Titles Python Data Analytics Advanced Data Analytics Using Python Practical Machine Learning with Python Python Descriptors. Intro -- Table of Contents -- About the Author -- About the Technical Reviewers -- Introduction -- Chapter 1: Introduction to Computing with Python -- Environments for Computing with Python -- Python -- Interpreter -- IPython Console -- Input and Output Ca