# Comparing with Python

**URL:** <https://discourse.julialang.org/t/comparing-with-python/11119>\
**Category:** General Usage\
**Created:** [May 24, 2018, 2:14am UTC](https://discourse.julialang.org/t/comparing-with-python/11119 "2018-05-24T02:14:01Z")\
**Posts on this page:** 1\
**Showing post:** 17

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**Author:** ![DNF](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/dnf/32/10191_2.png) [@DNF](https://discourse.julialang.org/u/DNF)\
**Post date:** [May 25, 2018, 10:26am UTC](https://discourse.julialang.org/t/comparing-with-python/11119/17 "2018-05-25T10:26:40Z")

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You can find a lot of discussion that is relevant in this thread: [“Julia motivation: why weren’t Numpy, Scipy, Numba, good enough?”](https://discourse.julialang.org/t/julia-motivation-why-werent-numpy-scipy-numba-good-enough/2236/20)

Many points are brought up. For me, the focus on first class numerical programming support is among the most important. Numpy’s syntax is pretty awkward compared to Julia.

I have really come to love multiple dispatch, which relieves me of almost all the tedious input parsing that you encounter in Python (and even much more so in Matlab!) I would estimate that about 25% of all coding time I’ve spent in Matlab was on input parsing, or figuring out how to efficiently branch my code based on the type of a variable.

Lightweight or zero-cost abstractions are great! You can choose an appropriate level of abstraction without sacrificing speed, and get very elegant and readable code.

**EDIT:** Oh, and of the remaining 75% of programming time, I’d say most of it was spent figuring out insane code vectorization tricks, to help performance. This is pretty much the same in Numpy.

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