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Learn MorePros of Julia
Pros of NumPy
Pros of Julia
- Fast Performance and Easy Experimentation25
- Designed for parallelism and distributed computation22
- Free and Open Source19
- Dynamic Type System17
- Calling C functions directly17
- Multiple Dispatch16
- Lisp-like Macros16
- Powerful Shell-like Capabilities10
- Jupyter notebook integration10
- REPL8
- String handling4
- Emojis as variable names4
- Interoperability3
Pros of NumPy
- Great for data analysis10
- Faster than list4
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Cons of Julia
Cons of NumPy
Cons of Julia
- Immature library management system5
- Slow program start4
- JIT compiler is very slow3
- Poor backwards compatibility3
- Bad tooling2
- No static compilation2
Cons of NumPy
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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 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.