# General questions from Python user

**URL:** <https://discourse.julialang.org/t/general-questions-from-python-user/55475>\
**Category:** Performance\
**Created:** [February 17, 2021, 5:23pm UTC](https://discourse.julialang.org/t/general-questions-from-python-user/55475 "2021-02-17T17:23:32Z")\
**Posts on this page:** 1\
**Showing post:** 31

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**Author:** ![jling](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jling/32/212909_2.png) [@jling](https://discourse.julialang.org/u/jling)\
**Post date:** [February 22, 2021, 8:46pm UTC](https://discourse.julialang.org/t/general-questions-from-python-user/55475/31 "2021-02-22T20:46:09Z")

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> [@Eben60](#):
>
> An example of a slow-down by a factor of 70

respect to a more optimal Julia code, sure. But what about when compared to Python / Numpy? Your last statement was:

> [@Eben60](#):
>
> **much** slower (e.g. naive Julia against numpy)

but that [post](https://discourse.julialang.org/t/problem-with-huge-number-of-memory-allocations-in-for-loops/55714) was about C++. Now, I think everyone can agree there are more quicks in C++ than Python or Julia.

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