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

Using simple statistics to better understand our data

Now that it's clear how the data is structured and what is contained in the collection, we can get a better understanding by looking at some basic stats.

To get us started, let's invoke the describe function:

julia> describe(iris)

The output is as follows:

This function summarizes the columns of the irisDataFrame. If the columns contain numerical data (such as SepalLength), it will compute the minimum, median, mean, and maximum. The number of missing and unique values is also included. The last column reports the type of data stored in the row.

A few other stats are available, including the 25th and the 75th percentile, and the first and the last values. We can ask for them by passing an extra stats argument, in the form of an array of symbols:

julia> describe(iris, stats=[:q25, :q75, :first, :last])

The output is as follows:

Any combination of stats labels is accepted. These are all the options—:mean, :std, :min, :q25, :median, :q75, ...