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I tried Julia in 2018 as my new programming language to learn that year. The syntax is clean and simple, and I remember it as a "Pascal for scientific computing
by jll29 6y ago
I tried Julia in 2018 as my new programming language to learn that year. The syntax is clean and simple, and I remember it as a "Pascal for scientific computing" - as easy to read as Python yet faster execution.
I liked the static type checking and compilation, but found it frustrating at the time that the sample code from a book I had purchased didn't work; overall, the language seemed to be still too much in flux to be entrusted with an important project, although I have spoken with fellow scientists who use it as their main language for experimentation and are happy.
Python is clearly winning because most important libraries are C++ under the bonnet - or even Cython (which is used e.g. by SpaCy), and its ecosystem with IPython, Jupyter notebooks and comprehensive data science libraries (pandas, matplotlib, sklearn, numpy, ...).
- sidpatil 6y agoPython has had a much longer head start, so it's no surprise it's still widely popular. You also bring up a good point regarding Julia's changing syntax and behavior—I've encountered the same issue trying copypasted code from various tutorials online (they didn't work.) Julia's package ecosystem is rapidly catching up with Python's, at least for the packages you mentioned. I.e. Julia offers DataFrames.jl (its Pandas competitor), Plots and Gadfly (its Matplotlib competitors), and a few different ML libraries (its sklearn competitor). As for Numpy, its functionality is already offered through the base library in Julia, so there's no need for a separate package. One especially nice feature of Julia is its interoperability with other languages. For example, if you need to use Python-specific functionality, you can do so from Julia through the PyCall library. Likewise for R, MATLAB, Java, etc. (C and Fortran FFIs are already offered through the base library.)