Julia is a high-level, high-performance dynamic programming language for technical computing, designed to combine the speed of C with the ease of use of Python, featuring just-in-time (JIT) compilation via LLVM, multiple dispatch as its core paradigm, and first-class support for parallel and distributed computing.
What This Cheat Sheet Covers
This topic spans 23 focused tables and 188 indexed concepts, 158 flashcards. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
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An interactive map of every table and concept in this topic.
Basic Syntax & Numeric Types
| Concept | Example | Description | |
|---|---|---|---|
x = 5; y = 3.14 | Dynamic typing with optional type annotations | ||
Int8, Int16, Int32, Int64, Int128 | • Signed integers • Int64 default on 64-bit systems | ||
UInt8, UInt16, UInt32, UInt64, UInt128 | Unsigned integer types for positive values | ||
Float16, Float32, Float64 | • Floating-point numbers • Float64 is default precision | ||
n = big(2)^1000 | Arbitrary precision integers via GMP library | ||
setprecision(256); big(π) | Arbitrary precision floats via MPFR library | ||
x::Int64 = 42 | Optional type constraint for variables | ||
# single line #= multi line =# | Single and multi-line comment syntax | ||
α = 1; β = 2; δ = α + β | Use Greek letters via \alpha<TAB> | ||
∑ = 0; ∫ = π/2 | Math symbols like \sum<TAB> and \int<TAB> |