We will start by introducing the basic syntax of the language, and quickly move into the details of how Julia is different from other scripting languages and how to exploit Julia's type system + multiple dispatch to be able to achieve C/Fortran-like performance while maintaining the concise syntax of a scripting language. "The quick brown fox jumps over the lazy dog α,β,γ"# the second argument is equivalent to the second argument of split, see below#> "The quick red fox jumps over the lazy dog α,β,γ"# search and replace can also take a regular expressions by preceding the string with 'r'#> "The quick red fox jumps over the lazy dog α,β,γ"# there are also functions for regular expressions that return RegexMatch types# RegexMatch types have a property match that holds the matched string#> SubString{String}["quick", "brown", "jumps", "over", "lazy"]# e.g., with one argument it strips the outer whitespace# or with a second argument of an array of chars it strips any of them;# (the last two arguements are limit and include_empty, see docs)# since this array has no type, functions like push! at the end of a function name indicates that the first argument is updated.#> 1-element Array{Array{Int64,1},1}: Array{Int64,1}[[1, 2, 3]]#> 8-element Array{Int64,1}: [1, 1, 2, 2, 3, 3, 4, 4]#> 8-element Array{Int64,1}: [4, 3, 2, 1, 4, 3, 2, 1]#> [1] top-level scope at C:\JuliaByExample\src\error_handling.jl:5#> [3] include_relative(::Module, ::String) at .\loading.jl:1038#> [4] include(::Module, ::String) at .\sysimg.jl:29#> [5] exec_options(::Base.JLOptions) at .\client.jl:229#> 12×3 Array{Int64,2}: [1 1 1; 2 1 1; 1 2 1; 2 2 2; 1 3 2; 2 3 2; 1 1 3; 2 1 3;# Julia comprehensions are another way to easily create#> 2×3×2 Array{LegacyStrings.ASCIIString,3}: LegacyStrings.ASCIIString[#> "Hi Im element # 1" "Hi Im element # 3" "Hi Im element # 5";#> "Hi Im element # 2" "Hi Im element # 4" "Hi Im element # 6"]#> LegacyStrings.ASCIIString["Hi Im element # 7" "Hi Im element # 9"#> "Hi Im element # 11"; "Hi Im element # 8" "Hi Im element # 10" "Hi Im element # 12"]# to be applied to specific dimensions of an array:# when you combine arrays of different sizes in an operation,# an attempt is made to "spread" or "broadcast" the smaller array# so that the sizes match up.

From JuliaCon recordings to virtual meetups on technical topics, our YouTube channel hosts much of the existing community created Julia content. (df::DataFrame,index::Int64,item::AbstractArray{T,1},name::Symbol)

The simplest possible script. Check out the Julia Track on Want to just give it a shot and dive right into the Julia documentation? not very intuititive to look at# (for more examples of try, catch see Error Handling above)# this will cause an error, you have to assign the correct type# this will cause an error, you have to assign the right shape#> DimensionMismatch("tried to assign 3×2 array to 2×3×1 destination")#> Dict{Int64,String} with 2 entries: Dict(2=>"two",1=>"one")#> Dict{Int64,String} with 3 entries: Dict(2=>"two",3=>"three",1=>"one")# (note dicts cannot be assumed to keep their original order)# dicts may also be created with the type explicitly set#> Dict{Int64,AbstractString} with 1 entry: Dict{Int64,AbstractString}(0=>"zero")#> Dict{Int64,String} with 10 entries: Dict(7=>"7",4=>"4",9=>"9",10=>"10",# as you would expect, Julia comes with all the normal helper functions#> Base.ValueIterator for a Dict{Int64,String} with 3 entries: ["two", "three", "one"]#> 3-element Array{String,1}: ["two", "three", "one"]# (AFAIK, the two are interchangable in this context)# if the array is being manipulated during evaluation a while loop shoud be used# dicts may be looped through using the keys function:# (note enumerate starts from 1 since Julia arrays are 1 indexed unlike python)# Numbers can be compared with opperators like <, >, ==, !=# More complex logical statments can be achieved with `elseif`# For simple logical statements, one can be more terse using the "ternary operator",# which takes the form `predicate ? They assume you already have Julia installed and working Check them out to There also exist a growing number of curated Julia courses in the form of video lecture series. We thank Fastly for their generous infrastructure support. The Julia Programming Language website is built with Franklin.jl - a native Julia website generator.

There are also a few MOOC's that have been created using Julia. ©2020 JuliaLang.org contributors.The content on this website is made available under the MIT license. Julia provides ease and expressiveness for high-level numerical computing, in the same way as languages such as R, MATLAB, and Python, but also supports general programming. Julia is really a great tool and is becoming an increasingly popular language among the data scientists. 2 CHAPTER 1.

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