Book Image

Learning Julia

By : Anshul Joshi, Rahul Lakhanpal
Book Image

Learning Julia

By: Anshul Joshi, Rahul Lakhanpal

Overview of this book

Julia is a highly appropriate language for scientific computing, but it comes with all the required capabilities of a general-purpose language. It allows us to achieve C/Fortran-like performance while maintaining the concise syntax of a scripting language such as Python. It is perfect for building high-performance and concurrent applications. From the basics of its syntax to learning built-in object types, this book covers it all. This book shows you how to write effective functions, reduce code redundancies, and improve code reuse. It will be helpful for new programmers who are starting out with Julia to explore its wide and ever-growing package ecosystem and also for experienced developers/statisticians/data scientists who want to add Julia to their skill-set. The book presents the fundamentals of programming in Julia and in-depth informative examples, using a step-by-step approach. You will be taken through concepts and examples such as doing simple mathematical operations, creating loops, metaprogramming, functions, collections, multiple dispatch, and so on. By the end of the book, you will be able to apply your skills in Julia to create and explore applications of any domain.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
8
Data Visualization and Graphics

Understanding DataFrames


A DataFrame is a data structure that has labeled columns, which may individually have different data types. Like a SQL table or a spreadsheet, it has two dimensions. It can also be thought of as a list of dictionaries, but fundamentally, it is different.

DataFrames are the recommended data structure for statistical analysis. Julia provides a package called DataFrames.Jl, which has all the necessary functions to work with DataFrames.

Julia's package, DataFrames, provides three data types:

  • NA: A missing value in Julia is represented by a specific data type, NA.
  • DataArray: The array type defined in the standard Julia library, though it has many features, doesn't provide any specific functionalities for data analysis. The DataArray type provided in DataFrames.jl provides such features (for example if we needed to store some missing values in the array).
  • DataFrame: This is a two-dimensional data structure, such as spreadsheets. It is much like R or Pandas DataFrames and provides...