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The official website for the Julia Language. Julia is a language that is fast, dynamic, easy to use, and open source. Click here to learn more.
19. Jan. 2023 · Python for Data Science: https://ibm.biz/Python_for_Data_SciencePython and Julia are both common and powerful language that may seem alike, but there are def...
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23. März 2021 · The main takeaway is that with Julia, you no longer need to vectorize to improve performance. In fact, good use of loops might deliver the best performance. In this post, I am adding Python to the mix. The language of choice of Data Scientists has a word to say. We will solve a very simple problem where built-in implementations are available ...
Marking and Generational Collection. Julia’s mark phase is implemented through a parallel iterative depth-first-search over the object graph. Julia’s collector is non-moving, so object age information can’t be determined through the memory region in which the object resides alone, but has to be somehow encoded in the object header or on a ...
Noteworthy differences from R. One of Julia's goals is to provide an effective language for data analysis and statistical programming. For users coming to Julia from R, these are some noteworthy differences: Julia's single quotes enclose characters, not strings. Julia can create substrings by indexing into strings.
In Julia, a function is an object that maps a tuple of argument values to a return value. Julia functions are not pure mathematical functions, because they can alter and be affected by the global state of the program. The basic syntax for defining functions in Julia is: julia> function f(x,y) x + y. end f (generic function with 1 method) This ...
New language features. New Memory type that provides a lower-level container as an alternative to Array. Memory has less overhead and a faster constructor, making it a good choice for situations that do not need all the features of Array (e.g. multiple dimensions). Most of the Array type is now implemented in Julia on top of Memory, leading to ...
