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...

The papermill package provides a simple command-line interface that interprets and then executes a Jupyter notebook and then stores the results in a new notebook file. In this recipe, we gave the first argument – the input notebook file –sample.ipynb and the second argument – the output notebook file –output.ipynb. The tool then executes the code contained in the notebook and produces the output. The notebook's file format keeps track of the results of the last run, so these results are added to the output notebook and stored at the desired location. In this recipe, this is a simple local file, but papermill can also store to a cloud location such as Amazon Web Services (AWS) S3 storage or Azure data storage.

In step 2, we added the --kernel python3 option when using the papermill command-line interface. This option allows us to specify the kernel that is used to execute the Jupyter notebook. This might be necessary to prevent...