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 the HTTP response

Armed with a good understanding of Julia's dictionary data structure, we can now take a closer look at the headers property of resp, our HTTP response object.

To make it easier to access the various headers, first let's convert the array of Pair to a Dict:

julia> headers = Dict(resp.headers) 
Dict{SubString{String},SubString{String}} with 23 entries: 
"Connection"     => "keep-alive" 
  "Via"          => "1.1 varnish (Varnish/5.1), 1.1 varnish (Varni... 
  "X-Analytics"  => "ns=0;page_id=38455554;https=1;nocookies=1" 
#... output truncated... #

We can check the Content-Length value to determine whether or not we have a response body. If it's larger than 0, that means we got back a HTML message:

julia> headers["Content-Length"] 

It's important to remember that all the values in the headers dictionary are strings, so we can't go comparing them straight away:

julia> headers["Content-Length"] > 0 
ERROR: MethodError: no method matching isless(...