Dask provides interfaces for NumPy arrays, as well as the DataFrames shown in this recipe. There is also a machine learning interface called dask_ml that exposes similar capabilities to the scikit-learn package. Some external packages, such as xarray, also have a Dask interface. Dask can also work with GPUs to further accelerate computations and load data from remote sources, which is useful if the computation is distributed across a cluster.
Applying Math with Python
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Applying Math with Python
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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.
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Current Title:
Applying Math with Python
Table of Contents (12 chapters)
Preface
Basic Packages, Functions, and Concepts
Free Chapter
Mathematical Plotting with Matplotlib
Calculus and Differential Equations
Working with Randomness and Probability
Working with Trees and Networks
Working with Data and Statistics
Regression and Forecasting
Geometric Problems
Finding Optimal Solutions
Miscellaneous Topics
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