#### 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.
Table of Contents (12 chapters)
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 xarray package provides the DataArray andDataSet classes, which are (roughly speaking) multi-dimensional equivalents of the Pandas Series and DataFrame objects. We're using a dataset in this example because each index – a tuple of a date and location – has two pieces of data associated with it. Both of these objects expose a similar interface to their Pandas equivalents. For example, we can compute the mean along one of the axes using the mean method. The DataArray and DataSet objects also have a convenience method for converting into a Pandas DataFrame called to_dataframe. We used it in this recipe to convert to a DataFrame for plotting, which isn't really necessary because xarray has plotting features built into it.

The real focus of this recipe is on the to_netcdf method and the load_dataset routine. The former stores a DataSet in NetCDF format file. This requires the NetCDF4 package to be installed as it allows us to access...