# Examples/tutorials for using PyPlot (directly)

**URL:** <https://discourse.julialang.org/t/examples-tutorials-for-using-pyplot-directly/8486>\
**Category:** General Usage\
**Created:** [January 20, 2018, 12:22am UTC](https://discourse.julialang.org/t/examples-tutorials-for-using-pyplot-directly/8486 "2018-01-20T00:22:53Z")\
**Posts on this page:** 1\
**Showing post:** 4

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**Author:** ![jlperla](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jlperla/32/34332_2.png) [@jlperla](https://discourse.julialang.org/u/jlperla)\
**Post date:** [January 21, 2018, 3:13pm UTC](https://discourse.julialang.org/t/examples-tutorials-for-using-pyplot-directly/8486/4 "2018-01-21T15:13:05Z")

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Because it takes 30 to 40 seconds after opening ANY jupyter notebook to get the first plot (after precompiling everything possible) . That time is perfectly fine when trying to generate a production quality graph while doing a big computational project, but it is **unbearable** and **unnecessary** when learning Julia and flipping through various notebooks, or doing quick and dirty data exploration. I am trying to get grad students who know matlab, and undergraduates who may know nothing, to use Julia and I am pretty sure that this will be the one thing that turns them off.

For now, if I can tell them to use PlotlyJS and PyPlot directly when doing exploratory work, it cuts things down to 8ish seconds. They can use Plots.jl or whatever WAY down the road when they need to generate papers and slides (ie never for many of the students)

> [@Fastest time-to-plot for Jupyter demos/tutorials (after precompilation, but from otherwise cold start)?](https://discourse.julialang.org/t/fastest-time-to-plot-for-jupyter-demos-tutorials-after-precompilation-but-from-otherwise-cold-start/8114/6):
>
> @time begin using PlotlyJS plot(scatter(x=[1,2,3], y=[1,2,3]), Layout()) end takes 6.5 sec (on my desktop) and works on JuliaBox. Edit: Whoops – I misread the original question and thought it was using Plots.jl.

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