#### 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...

Dask builds a task graph for the computation, which describes the relationships between the various operations and calculations that need to be performed on the collection of data. This breaks down the steps of the calculation so that calculations can be done in the right order across the different workers. This task graph is then passed into a scheduler that sends the actual tasks to the workers for execution. Dask comes with several different schedulers: synchronous, threaded, multiprocessing, and distributed. The type of scheduler can be chosen in the call to the compute method or set globally. Dask will choose a sensible default if one is not given.

The synchronous, threaded, and multiprocessing schedulers work on a single machine, while the distributed scheduler is for working with a cluster. Dask allows you to change between schedulers in a relatively transparent way, although for small tasks, you might not get any performance benefits because of...