# My mental load using Julia is much higher than, e.g., in Python. How to reduce it?

**URL:** <https://discourse.julialang.org/t/my-mental-load-using-julia-is-much-higher-than-e-g-in-python-how-to-reduce-it/18902>\
**Category:** General Usage\
**Tags:** question, python, workflow\
**Created:** [December 21, 2018, 3:49pm UTC](https://discourse.julialang.org/t/my-mental-load-using-julia-is-much-higher-than-e-g-in-python-how-to-reduce-it/18902 "2018-12-21T15:49:44Z")\
**Posts on this page:** 1\
**Showing post:** 4

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**Author:** ![cstjean](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/cstjean/32/1444_2.png) [@cstjean](https://discourse.julialang.org/u/cstjean)\
**Post date:** [December 21, 2018, 4:32pm UTC](https://discourse.julialang.org/t/my-mental-load-using-julia-is-much-higher-than-e-g-in-python-how-to-reduce-it/18902/4 "2018-12-21T16:32:27Z")

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> [@jules](#):
>
> In Julia, this is much harder. Because methods never come bundled with the kinds of objects they’re meant to be used on, I see myself in front of a sea of functions without an idea what I have available. I know that there is `methodswith` , but this gets tiresome pretty fast if you have to use it a lot. My mental load is always relatively high, trying to remember, what was this function that I could call with a `DataFrame` as the first argument that did something related to aggregating, instead of doing `df.` and filtering the suggestions quickly.

Yeah, the problem of method discovery has been discussed before, but I don’t think that a solution has been found. You just have to get used to reading the documentation. It’s one of the drawbacks of multiple dispatch.

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