Introduction to Julia Language
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The document provides an introduction to the Julia programming language, highlighting its features and applications in data science, machine learning, and scientific computing. It outlines the installation process, core syntax, data types, function definitions, control flow, and libraries available for various purposes. Additionally, it discusses integration with Python and R, as well as important community projects.
![Code Examples
Simple Example Preprocess and Plot Data
Import Packages
using RDatasets,DataFrame, Gadfly
Load some data
mtcars = dataset("datasets", "mtcars")
Filter Models by Horse Power
mtcars = mtcars[mtcars[:HP] .> 100, :Model]
Plot
plot(mtcars, x=:Model, y=:HP, Geom.bar)](https://image.slidesharecdn.com/2016-08-25-introduction-to-julia-language-160901112519/85/Introduction-to-Julia-Language-13-320.jpg)
Introduction to Julia Language
- 1. Introduction to Presented by Diego Marinho de Oliveira Short Version Lead Data Scientist Date: 2016-08-25 Julia Language for Data Science, Machine Learning, Statistics, Mathematics
- 2. Summary Introduction Interactive/Development Environments Syntax Libraries Highlights Code Examples Integration Community Important Events/Projects
- 3. Introduction Julia is a high-level, high-performance dynamic programming language for technical computing Adequate for Data Science, Research, Web application, scientific computer, among others Multi-paradigm: multiple dispatch ("object-oriented"), procedural, functional, meta, multistaged High-Performance JIT Compiler First appeared in 2012 Stable release 0.4.6 June 2016 Julia paper: http://arxiv.org/pdf/1411.1607v3.pdf
- 5. Introduction: How to Install Julia Install Julia version 0.4.6 (last stable version) Windows https://s3.amazonaws.com/julialang/bin/winnt/x64/0.4/julia-0.4.6-win64.exe Mac OS X https://s3.amazonaws.com/julialang/bin/osx/x64/0.4/julia-0.4.6- osx10.7+.dmg Ubuntu sudo add-apt-repository ppa:staticfloat/juliareleases sudo add-apt-repository ppa:staticfloat/julia-deps sudo apt-get update For more details check http://julialang.org/downloads/platform.html
- 6. Interaction/Development Environments Julia Studio / Forio Julia REPL Kaggle Kernels IJulia JuliaBoxAtom julia-vim
- 7. Syntax: Getting Started Julia run code Call Julia script julia script.jl arg1 arg2… Create a Julia command on the shell julia -e 'for x in ARGS; println(X); end' foo bar Create a Julia command using UTF-8 characters echo 'println("Greetings! 你好! 안녕하세요?")' > ~/.juliarc.jl
- 8. Syntax: Variables Example Variables A variable x = 10 Float variable y = x + 2.0 UTF-8 variable 𝛔 = 1 Built-in Types Int8, Int16, Int32, Int64, Int12, UInt32, UInt64, UInt128 103 Bool false, true AbstractString “Data!” Char ‘Z’ Float16, Float32, Float64 Complex 1 + 2im Rational 5//6 Some math functions round(Int, 76.0) floor, ceil, trunc, eps, ... div, rem, mod, gcd, lcm, ... abs, sqrt, cbrt, exp, log, log2, ... sin, cos, tan, cot, sec, hypot, ... beta, gamma, eta, zeta, ...
- 9. Syntax: Strings Types AbstractString UTF8String ASCIIString Char Simple Usage Examples Using a char a = ‘A’ Simple string b = ”for Data Science” Interporlation println(”Julia is $b”) Regular Expression match(r”(Ww+){,2}”, b) Unicode usage println(“u2200 x u2203 y”) Triple Quote json = ”””{ “Id”: 10232 }””” Concat “Lets” * “ code” Repeat sentence repeat(“Julia”, 10)
- 10. Syntax: Functions, Control Flow Functions Basic function definition function f(x,y) x + y end Terse function definition f(x,y) = x+y Optional and keywords args x: optional; a: keyword f(x, y=1; a=3) = 1 Control Flow Compound expressions Z = begin f(x,y) x+ 1 x + y end Repeated eval loops while x < 1 for x=1:10 Short-circuit evaluation &&, || Conditional evaluations If x < 10 x += 1 elseif 10 <= x < 12 x+= 2 else x += 3 end Exception Handling try - catch Tasks (coroutines) yieldto
- 11. Syntax: Types, Parallel and Packages Types Abstract type abstract Integer <: Real Create a composite type type Point x::Float64 y::Float64 end Parallel Execute parallel command nheads = @parallel (+) for i=1:200000000 Int(rand(Bool)) end Packages Show status Pkg.status() Install a new package Pkg.add(“<Package Name>”) Remove Package Pkg.rm(“Package”) Install from GitHub Pkg.clone(“Package”) Update packages Pkg.update()
- 12. Library Highlights ● DataFrames.jl (data analysis) ● Gadfly.jl (data visualization) ● Vega.jl (data visualization) ● HypothesisTests.jl (statistics) ● XGBoost.jl (machine learning) ● GLM.jl (machine learning) ● Mocha.jl (machine learning/deep learning) ● Low Rank Models.jl (machine learning) ● JuMP.jl (optimization)
- 13. Code Examples Simple Example Preprocess and Plot Data Import Packages using RDatasets,DataFrame, Gadfly Load some data mtcars = dataset("datasets", "mtcars") Filter Models by Horse Power mtcars = mtcars[mtcars[:HP] .> 100, :Model] Plot plot(mtcars, x=:Model, y=:HP, Geom.bar)
- 14. Integrations Python: PyCall Use math from Python using PyCall @pyimport math math.sin(math.pi / 4) - sin(pi / 4) Use Python @pyimport matplotlib.pyplot as plt x = linspace(0,2*pi,1000); y = sin(3*x + 4*cos(2*x)); plt.plot(x, y, color="red", linewidth=2.0, linestyle="--") plt.show()
- 16. Community Lang S Stats Opt Parallel DB Quantum Astro GPU Finance Sparse Math ...among others