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Welcome back.

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Hopefully you've created your own graph here and of a plot or figure.

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See.

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Again I'm mispronouncing all these different words.

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Hopefully your prices aren't cheaper because they're not but a game is very competitive these days.

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So the last video we finished off on bar graphs there is actually a horizontal bar graph.

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You may have seen this before.

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So if we go fig X equals plot dot subplots and we go axe DOB bar Hage for horizontal we're going to

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pass it just the exact same thing as above now.

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But our prices dot ks and nut but because we've got our saved dictionary up there.

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Yours might be a different name dot values.

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Oh what's happened here.

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Uncatchable type decays.

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Mm hmm.

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I actually did look this one up in this case for the bar horizontal we have to turn our values here

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into a list.

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Now had I find that well I did our search method I looked it up pre banked it I had to make sure that

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I was getting things right for this one since we messed up in the last lecture but that's okay so I

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go there we've got a horizontal bar same information just presented horizontally now.

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Which one should you use.

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It's more of a personal preference here.

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I like this one.

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It's vertical.

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Sometimes you'll want a horizontal bar.

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There are other plots that look better on a horizontal.

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We'll have a look at some of those in potentially a future video.

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Now let's check out.

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What have we done so far.

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We've done line we've done scatter we Dunbar let's do his diagrams what's a histogram.

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Well let's see I first remember.

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If in doubt run the code I'll make some data we'll go.

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X equals and paid a random dot Rand in and we want a thousand of what is Rand end to shift tab.

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Return a sample or samples from the standard normal distribution.

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What is this standard normal distribution.

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Let's have a look.

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Normal Distribution

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go here.

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Okay.

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In probability fearing the normal or Gaussian distribution is very common continuous probability distribution.

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Well that was a bit of a mouthful but what you really need to know is a normal distribution are these

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curves here.

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So in the middle here this is the main and the distance between the main and here is a variance.

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And so whenever someone refers to a normal distribution they're referring to one of these kind of like

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symmetrical curves and they can appear anywhere on the x axis like this.

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That's what you need to know about a normal distribution.

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So if we go in here this is going to make some a thousand random samples that would fit within this

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curve.

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But if in doubt run the code let's actually see this in action.

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Fig X equals because remember every time we want a new plot we create a new plot subplots X dot hist

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X little semicolon at the end.

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So what we've done here is we're doing a histogram but oftentimes you'll see it shortened to hist so

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you see hist probably referring to histogram.

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So if we do that huh.

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There we go.

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We've got a bumpy curve it's not as smooth as this one but you can kind of see it's got the same shape.

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That's what we're after.

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Just that same shape.

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Beautiful.

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Okay.

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Now we've covered histogram is now.

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What if you wanted to get multiple plots happening at the same time we've referred to this before as

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subplots.

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Well the beautiful thing is we can make them with the command we've been using.

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Now again there's two options for actually histogram because you want to make this a bit more communicated

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and plot it.

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There we go.

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We're in the habit of being communicative.

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So you're gonna finish off that cell before we suddenly one two options for subplots.

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Let's do that make a little heading and then we'll go subplot option one.

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Wonderful.

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Okay.

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We're going to create Fig.

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Now this is gonna look a bit different to what we've been doing.

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AX 1 ax 2 new light wall what's going on here Daniel.

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But don't worry.

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Follow along and we'll explain in a second because remember when the habit of just seeing what happens

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with the code and Rosie goes to hold on.

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We never passed a keyword argument to this.

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Now we're gonna pass three living dangerously I told you.

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Big size equals ten five.

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Well my five key is a bit sticky you know.

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So if we look what is this actually doing.

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Let's run it

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look at that now can we infer what's happening here without even looking at the doctoring so we've got

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fig comma and then a tuple which is of tuples of x 1 x 2 acts 3 X 4.

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So can you guess what's happening here and we've got some keyword arguments and rows equals two and

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Coles equals two fig size equals 10 five.

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What do you think's happening.

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And rows equals to two rows two columns.

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I've got X one maybe x two x three x four.

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I think that would be pretty spot on if that's what you guessed.

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Said I guessed.

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So here we go.

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Let's read create a figure instead of subplots this utility.

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Yeah we've read that before.

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And rows number of rows columns of the subplot grid beautiful.

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Now let's see how would we get data on here.

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So let's let's do this plot to each different axis.

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Let's say this inaction asks one dot plot.

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Let's go to X you've got some X data I think that'll probably do.

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Let's see what happens when we do plot it bam straight line.

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Wonderful.

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We can do that okay.

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So yeah x 1 is this top left 1.

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Okay.

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That's that makes sense because it comes first.

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Can you guess which one is x 2.

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Well let's see and find out empty.

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Let's make some some more random data.

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Random random 10 and we'll do random random again 10 and semicolon huh.

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Okay.

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And see we've tried a little scatter here.

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So that's x 2 wonderful x ray.

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Let's do a bar on this one.

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We're gonna start mixing this up you know not but can I do tab complete.

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Not better prices.

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Yes I can.

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Doug K's not but we want the values on this one dollar values.

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Wonderful.

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Let's see what happens.

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Beautiful we're starting to feel an alpha subplot here.

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This is wonderful access for we want haste and then we want and paid a random grand and normal distribution

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we take that out before there we go.

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Now we have a subplot with four different subplots.

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Now again this is just some dummy data except for this.

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This is real stuff right here.

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This is Dan's arm about a store and now you can add some titles to these actresses much like we've done

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up here with this set command here.

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But this is just to exemplify one way.

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Option one would do option two in a moment maybe in the next video of how you can get more information

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on the one figure.

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So if we look back to our anatomy here it's still a big figure.

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This blue square if we converted this blue square it's going to follow around this.

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But now we've got axes zero axes one axes three axes four.

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But it's actually actually zero one two three.

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But we've named them differently.

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In reality we should have probably zero indexes but I've just gone one two three four.

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Now you're starting to see if you wanted to get more information just one image rather than having four

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different figures you can say you can do that with subplots.

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The reason why we've been using this command is that you can create single plots or multiple plots with

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the same command.

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That's that flexibility we were talking about of the object oriented API for map plot lib.

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So this is option one for the video I guess too long will pause it there and we'll have a look at option

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2 and the next one.