# 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: ![JackDevine](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jackdevine/32/1048_2.png) [@JackDevine](https://discourse.julialang.org/u/JackDevine)
#### Post date: [June 18, 2017, 6:11am UTC](https://discourse.julialang.org/t/julia-motivation-why-werent-numpy-scipy-numba-good-enough/2236/42 "2017-06-18T06:11:17Z")

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This conversation may be a little dead, but I was also stumped by the “why not numba?” question when I gave a talk on Julia recently. So, I took the benchmarks from `test/perf/micro` and for all of the python benchmarks I added `@jit` decorations to the functions. This worked fine except for `parse_int` which gave me some error that I didn’t understand. After running the benchmarks, this is what I get:  
 ![](https://global.discourse-cdn.com/julialang/original/3X/e/f/efaa11ca6aefc2db7c38ee8913c0c82787673832.png)  
 ![](https://global.discourse-cdn.com/julialang/original/3X/c/3/c3a94a39c6b91e0aded15740487f52a6a92122fa.png)  
I am on a 2013 macbook pro with an intel i7 quad core (2.6GHz) and 16Gb of ram. The Julia benchmarks were on 0.6.0-rc2.0. As for Python/Numba, we have Python 3.5.2 and numba 0.33.0.

This shows you that Python is definitely outperformed by Julia, but when the Numba compiler works, you can write Python code that is within a factor of three of Julia performance.

There have been many interesting points made above about Julia vs Python/Numba as languages, but in terms of performance it seems that Numba can be quite competitive in tasks that are important in the field of numerical computing.

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