BEGINNING PYTHON
Gain a fundamental understanding of Python’s syntax and features with this revised introductory and practical reference. Covering a wide array of Python–related programming topics, including addressing language internals, database integration, network programming, and web services, you’ll be guided by sound development principles. Updated to reflect the latest in Python programming paradigms and several of the most crucial features found in Python 3, Beginning Python, Fourth Edition also covers advanced topics such as extending Python and packaging/distributing Python applications. Ten accompanying projects will ensure you can get your hands dirty in no time. What You’ll Learn Become a proficient Python programmer by following along with a friendly, practical guide to the language’s key features Write code faster by learning how to take advantage of advanced features such as magic methods, exceptions, and abstraction Gain insight into modern Python programming paradigms including testing, documentation, packaging, and distribution Work through10 interesting projects, including a P2P file–sharing application, chat client, video game, remote text editor, and more Who This Book Is For Programmers, novice and otherwise, seeking a comprehensive introduction to the Python programming language.
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NUMERICAL PYTHON
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.
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
Who This Book Is For
Developers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis.
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MASTERING MATPLOTLIB
matplotlib is a Python plotting library that provides a large feature set for a multitude of platforms. Given the depth of the library’s legacy and the variety of related open source projects, gaining expert knowledge can be a time-consuming and often confusing process.
You’ll begin your exciting journey learning about the skills that are necessary in leading technical teams for a visualization project or to become a matplotlib contributor.
Supported by highly-detailed IPython Notebooks, this book takes you through the conceptual components underlying the library and then provides a detailed overview of its APIs. From there, you will learn about event handling and how to code for interactive plots.
Next you will move on to customization techniques, local configuration of matplotib, and then deployments in Cloud environments. The adventure culminates in an exploration of big data visualization and matplotlib clustering.