Название: Data Analysis with Rust NotebooksАвтор: Dr. Shahin Rostami
Издательство: Independently published
Год: 2022
Страниц: 109
Язык: английский
Формат: pdf
Размер: 10.1 MB
A practical book on Data Analysis with Rust Notebooks that teaches you the concepts and how they're implemented in practice.
The Rust programming language has become a popular choice amongst software engineers since its release in 2010. Besides being something new and interesting, Rust promised to offer exceptional performance and reliability. In particular, Rust achieves memory-safety and thread-safety through its ownership model. Instead of runtime checks, this safety is assured at compile time by Rust's borrow checker. This prevents undefined behaviour such as dangling pointers!
I had originally planned to use Plotters for all the graphing in this book. However, shortly after finding Plotters, I found out that a Rust library had enabled Plotly support. You will see this in later sections, but for now, here is an example of how Plotters works. In my other book, Practical Evolutionary Algorithms, I relied on the Plotly graphic libraries to generate visualisations throughout each notebook. When I started writing Rust Notebooks a Plotly solution was not available, however, I found Plotters to be a suitable alternative for rendering visualisations. Less than 24 hours after making that decision, a plotting library for Rust powered by Plotly.js was posted on Reddit and caught my attention.
The Ndarray crate provides us with a multidimensional container that can contain general or numerical elements. If you're familiar with Python, then you can consider it to be similar to the Numpy package. With Ndarray we get our - dimensional arrays, slicing, views, mathematical operations, and more. We'll need these in later sections to load in our datasets into containers that we can operate on and conduct our analyses.
Discounted Price that will grow as the book does,
All code examples in Rust,
Rust (Jupyter) Notebooks for each Section.
Get it now to enhance your work in Rust, NDArray, Data Science, Data Analysis, and Machine Learning.
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