Book Image

Julia Programming Projects

By : Adrian Salceanu
Book Image

Julia Programming Projects

By: Adrian Salceanu

Overview of this book

Julia is a new programming language that offers a unique combination of performance and productivity. Its powerful features, friendly syntax, and speed are attracting a growing number of adopters from Python, R, and Matlab, effectively raising the bar for modern general and scientific computing. After six years in the making, Julia has reached version 1.0. Now is the perfect time to learn it, due to its large-scale adoption across a wide range of domains, including fintech, biotech, education, and AI. Beginning with an introduction to the language, Julia Programming Projects goes on to illustrate how to analyze the Iris dataset using DataFrames. You will explore functions and the type system, methods, and multiple dispatch while building a web scraper and a web app. Next, you'll delve into machine learning, where you'll build a books recommender system. You will also see how to apply unsupervised machine learning to perform clustering on the San Francisco business database. After metaprogramming, the final chapters will discuss dates and time, time series analysis, visualization, and forecasting. We'll close with package development, documenting, testing and benchmarking. By the end of the book, you will have gained the practical knowledge to build real-world applications in Julia.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
About Packt

Loading and saving our data

Julia comes with excellent facilities for reading and storing data out of the box. Given its focus on data science and scientific computing, support for tabular-file formats (CSV, TSV) is first class.

Let's extract some data from our initial dataset and use it to practice persistence and retrieval from various backends.

We can reference a section of a DataFrame by defining its bounds through the corresponding columns and rows. For example, we can define a new DataFrame composed only of the PetalLength and PetalWidth columns and the first three rows:

julia> iris[1:3, [:PetalLength, :PetalWidth]] 
3×2 DataFrames.DataFrame 
│ Row │ PetalLength │ PetalWidth │ 
│ 1   │ 1.4         │ 0.2        │ 
│ 2   │ 1.4         │ 0.2        │ 
│ 3   │ 1.3         │ 0.2        │ 





The generic indexing notation is dataframe[rows, cols], where rows can be a number, a range, or an Array of boolean values where true indicates that the row should...