#### Overview of this book

Julia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia. Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia. By the end of the book, you will have acquired the skills to work more effectively with your data
Title Page
Dedication
Contributors
Preface
Variables, Types, and Functions
Julia Workflow
Distributed Computing
Other Books You May Enjoy
Index

## Creating a matrix from a set of vectors as rows

Julia has a powerful set of matrix manipulation features. However, since vectors are one-dimensional and matrices are two-dimensional, people often need to switch from one to the other. In this recipe, we show an example of how to create a matrix from a set of vectors as rows.

Let's assume that you have the following input dataset:

julia> input = [[10i+1:10i+5;] for i in 1:3]
3-element Array{Array{Int64,1},1}:
[11, 12, 13, 14, 15]
[21, 22, 23, 24, 25]
[31, 32, 33, 34, 35]

From that dataset, you wish to create the following matrix:

julia> output = [10i+j for i in 1:3, j in 1:5]
3×5 Array{Int64,2}:
11 12 13 14 15
21 22 23 24 25
31 32 33 34 35

Make sure that the input and output variables are defined as before in your Julia console before trying out the recipe so that you can test whether proper results are obtained.

### Note

In the GitHub repository for this recipe, you will find the commands.txt file that contains the presented...