#### Overview of this book

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain. The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
Preface
Basic Packages, Functions, and Concepts
Free Chapter
Mathematical Plotting with Matplotlib
Working with Randomness and Probability
Geometric Problems
Finding Optimal Solutions
Miscellaneous Topics
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# How it works...

There is a lot happening in this recipe, so let's start by explaining the overall process. Cython takes code that is written in an extension of the Python language and compiles it into C code, which is then used to produce a C extension library that can be imported into a Python session. In fact, you can even use Cython to compile ordinary Python code directly to an extension, although the results are not as good as when using the modified language. The first few steps in this recipe define the new version of the Python code in the modified language (saved as a .pyx file), which includes type information in addition to the regular Python code. In order to build the C extension using Cython, we need to define a setup file, and then we create a file that we run to produce the results.

The final compiled version of the Cython code runs considerably faster than its Python equivalent. The Cython compiled Python code (hybrid, as we called it in this recipe...