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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.
The Regional Environment and Agriculture Programming Model (REAP),
facilitates scenario-or "what if"-analyses by showing how changes
in technology, commodity supply or demand, or farm, resource,
environmental, or trade policy could affect a host of performance
indicators important to decisionmakers and stakeholders. This
report describes its theoretical and modeling system specification
and the data used by REAP, and serves as a user guide for setting
up and running model simulations.
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