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Julia

488
616
+ 1
156
NumPy

2K
712
+ 1
10
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Pros of Julia
Pros of NumPy
  • 21
    Fast Performance and Easy Experimentation
  • 21
    Designed for parallelism and distributed computation
  • 17
    Free and Open Source
  • 16
    Lisp-like Macros
  • 16
    Calling C functions directly
  • 16
    Dynamic Type System
  • 15
    Multiple Dispatch
  • 9
    Powerful Shell-like Capabilities
  • 7
    REPL
  • 7
    Jupyter notebook integration
  • 4
    String handling
  • 4
    Emojis as variable names
  • 3
    Interoperability
  • 8
    Great for data analysis
  • 2
    Faster than list

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Cons of Julia
Cons of NumPy
  • 5
    Immature library management system
  • 4
    Slow program start
  • 3
    JIT compiler is very slow
  • 3
    Poor backwards compatibility
  • 2
    Bad tooling
  • 2
    No static compilation
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    What is Julia?

    Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.

    What is NumPy?

    Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

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    What companies use Julia?
    What companies use NumPy?
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    What tools integrate with Julia?
    What tools integrate with NumPy?

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    What are some alternatives to Julia and NumPy?
    Python
    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.
    R Language
    R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.
    MATLAB
    Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.
    Rust
    Rust is a systems programming language that combines strong compile-time correctness guarantees with fast performance. It improves upon the ideas of other systems languages like C++ by providing guaranteed memory safety (no crashes, no data races) and complete control over the lifecycle of memory.
    Golang
    Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.
    See all alternatives