#### 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

## Deviation metrics

Metrics that help calculate the distance or similarity between two vectors are called deviation metrics. These metrics help us understand the relationship between the different vectors and the data in them.

You have to have the `StatsBase` package ready. This can be done by running `using StatsBase` in the REPL.

### How to do it...

1. For getting the number of elements in a vector that are exactly equal to a set of elements in a vector, we can use the `counteq()` function.

For example, consider the two vectors: a = [1, 2, 3, 4, 5, 6] and b = [4, 2, 3, 5, 6, 7].

The elements at the second and third indexes are equal to each other, so they will be returned as a result of the `counteq()` function:

```counteq(a,b)
```

The output would look like the following:

2. The `countne()` function does exactly the opposite of the `counteq()` function in the preceding step. It returns the number of elements that are not equal in both the vectors:

```countne(a,b)
```

The output would look like the following:

3. The...