#### Overview of this book

Want to handle everything that Julia can throw at you and get the most of it every day? This practical guide to programming with Julia for performing numerical computation will make you more productive and able work with data more efficiently. The book starts with the main features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll also show you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation. Later on, you’ll see how to optimize data science programs with parallel computing and memory allocation. You’ll get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform. This book includes recipes on identifying and classifying data science problems, data modelling, data analysis, data manipulation, meta-programming, multidimensional arrays, and parallel computing. By the end of the book, you will acquire the skills to work more effectively with your data.
Julia Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
Extracting and Handling Data
Metaprogramming
Statistics with Julia
Building Data Science Models
Working with Visualizations
Parallel Computing

## Distances

In statistics, the distance between vectors or data sets are computed in various ways depending on the problem statement and the properties of the data. These distances are often used in algorithms and techniques such as recommender systems, which help e-commerce companies such as Amazon, eBay, and so on, to recommend relevant products to the customers.

To get ready, the `Distances` library has to be installed and imported. We install it using the `Pkg.add()` function. It can be done as follows:

```Pkg.add("Distances")
```

Then, the package has to be imported for use in the session. It can be imported through the `using ...` command. This can be done as follows:

```using Distances
```

### How to do it...

1. Firstly, we will look at the Euclidean distance. It is the ordinary distance between two points in Euclidean space. This can be calculated through the Pythagorean distance calculation method, which is the square root of the square of the element-wise differences. This can be done using the...