# Julia motivation: why weren't Numpy, Scipy, Numba, good enough?

**URL:** <https://discourse.julialang.org/t/julia-motivation-why-werent-numpy-scipy-numba-good-enough/2236>\
**Category:** Community\
**Tags:** history\
**Created:** [February 22, 2017, 3:34pm UTC](https://discourse.julialang.org/t/julia-motivation-why-werent-numpy-scipy-numba-good-enough/2236 "2017-02-22T15:34:33Z")\
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**Author:** ![rdeits](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/rdeits/32/286_2.png) [@rdeits](https://discourse.julialang.org/u/rdeits)\
**Post date:** [February 22, 2017, 4:24pm UTC](https://discourse.julialang.org/t/julia-motivation-why-werent-numpy-scipy-numba-good-enough/2236/5 "2017-02-22T16:24:19Z")

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@kristoffer.carlsson agreed; that article about pyston was the first thing that came to my mind.

Another piece of the “why Julia” puzzle is the fact that Julia does not treat a specific set of built-in types differently. That is, I can make my own `type` or `immutable` that can do anything that Julia’s built-in types can do, and my type can be just as fast as the one provided with Julia (if I were as skilled at numerical computing as the devs, which I am not). This isn’t just a nice feature; it’s _essential_ to making things like [automatic differentiation](https://github.com/JuliaDiff/ForwardDiff.jl) and [other numeric types](https://github.com/JuliaMath/FixedPointNumbers.jl) fast and easy to use. It’s also why we have really nice abstractions like the idea that an [image](https://github.com/JuliaImages/Images.jl) is not a matrix of doubles or bytes but instead a matrix of _colors_. That works because Julia lets us define a whole family of [color-related datatypes](https://github.com/JuliaGraphics/ColorTypes.jl) which provide useful behavior and abstraction at little or no run-time cost. That’s been the biggest benefit of Julia, in my experience.

Oh, and also [JuMP](https://github.com/JuliaOpt/JuMP.jl). JuMP is great.

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