In Julia, there are a bunch of ways to do plotting and none of them are standard, so plotting is a little daunting at first. One package that we have already mentioned here is PyPlot.jl, which is a really good wrapper over the python matplotlib library. PyPlot.jl is very similar to matplotlib, which in turn is kind of similar to plotting in matlab. There are many other really good packages for plotting in Julia, like GR, PlotlyJS and Gadfly. Each of these has a slightly different way of doing things and comes with it’s own advantages and disadvantages.
This is where Plots.jl comes in, by itself, Plots.jl cannot do any plotting, but it can call other plotting libraries in Julia and get them to do the plotting. Essentially, Plots.jl is a unified interface for plotting in Julia and in my opinion it has the most intuitive interface out of all of the libraries that I have mentioned. The way that it works is that you load Plots.jl, tell it what backend you would like it to use and then start plotting. So for example, if you want to use the PyPlot backend to make a heatmap of random data sampled from [0,\,1], but you only want to see the data in the range [0,\,0.3] and you want to use the magma colormap, then you do the following:
using Plots; pyplot()
heatmap(rand(30, 30), clim=(0.0, 0.3), color=:magma)
If you then decide that you want to see the same thing with GR, then you type
gr() and remake your plot. I would personally recommend Plots.jl if you are just starting out and you don’t have a specific reason to use one of the other packages.
Also, definitely check out their documentation, because it is really good, although maybe not quite as extensive as something like matlab or matplotlib.