Book Image

Julia Cookbook

By : Raj R Jalem, Jalem Raj Rohit
Book Image

Julia Cookbook

By: Raj R Jalem, Jalem Raj Rohit

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.
Table of Contents (12 chapters)

Sampling


Sampling is the process where by sample units are selected from a large population for analysis. The sample should be selected such that the results and inferences generated from them should be fairly applicable and can be generalized to the population from which it was initially sampled. There are a lot of ways in which sampling can be done. We will discuss them in the How to do it... section.

Getting ready

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

How to do it...

  1. The simplest way of sampling is random sampling, where one can draw a random element from the array, which is the population. This kind of sampling is generally used for ensuring that selection bias does not happen. This kind of sampling can be done using the sample() function of the StatsBase package:

    sample(x)
    

    The output would look like the following:

  2. Now, as we have seen how to randomly sample an element from a population, we would now see how to randomly sample...