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In the NUM pi section we talked about how many Python data science packages are built off.

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PI a raise.

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The same goes for Matlock lib and one of the beautiful things about map plot lib is it's about as flexible

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as an umpire raise if you can dream up a way to visualize data.

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Chances are you can do it in map plot lib but there are some common kinds of visualizations and others

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or some more common ones you're more likely to come across and these plots are the ones like line scatter

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bar haste and subplots or plotting multiple visualizations on one finger.

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Let's have a look at some of these kinds of plots that you'll see more often plots of figures.

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Remember that words kind of interchangeable.

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Using pure num pi arrays so started off.

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So we making figures with NUM pi arrays will make this mark down so it's a little bit communicative

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and then we'll go import.

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If you haven't already done this we've done this at the top.

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I will do it again.

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Import num pi as MP.

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I will start off by create some data.

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X equals NDP dot Lin space 0 10 one hundred maybe go on hold on Daniel.

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We haven't seen Lin space.

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Well in that case remember what can you do.

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Did you get it.

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Well if you did it doesn't matter.

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We can press shift tab and see what Lin space actually does.

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To an MP Lin space.

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The start stop numb the case then the first three arguments we've got zero start tennis stop 100 as

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now.

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All right well let's have a look.

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Return evenly spaced numbers over a specified interval.

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Returns num evenly spaced samples calculated over the interval start stop.

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So it's gonna return num evenly spaced intervals between start and stops.

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What do you think will happen here.

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Well if in doubt run the code let's check it out.

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Well there's gonna be 100 samples so we don't see all that.

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Let's see the first 10 wonderful.

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Well let's say the first one hundred one on I'm going to taking risk aren't you.

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So there we go.

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We've got 100 different samples here 100 different floats values here between 0 and 10.

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So we start at zero.

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Goes all the way up to 10 wonderful or reduces back to 10 to save some space.

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Excellent.

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Now let's go plot the data.

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So remember we're going to create a fig and an ax or figure and axes but these are the common short

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names figure in accessing go plot subplots because this is the method that we're gonna be using axe

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stop plot x and let's do because plot takes x and y but we don't have a Y.

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Let's just do X to the power of two.

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Well let's see what happens.

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We'll put a little semicolon wonderful we get this beautiful curve line.

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So the default here in that plot layer when you call plot is a line plot.

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This a line plot.

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So and create a line plot area.

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So as you can see just a straight line.

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Now use the same data.

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Let's create a scatter plot.

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Use the same data because it's the most common types.

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Let's write these down.

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We want

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fly plot scatter plot bar plot history and history show for histogram and a subplot.

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They're the main types of plots we're gonna be focused on.

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Of course there are more but we could we go on all year for the amount of plot you could do a map plot

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lib.

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So we'll focus on the baseline ones and then you can build upon that knowledge so we want you same data

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to make a scatter plot.

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See what a scatter plot is.

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So if we want to recreate it we have to always reset fig and ax.

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If we're in a different cell equals P LTM subplots.

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Wonderful acts.

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Now we're gonna scatter so here we use dot plot but if we want to scatter plot we'll see what that looks

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like in a second.

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We need to call dot scatter on ax.

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So here we go.

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I want X and then we want end p.

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We'll do the exponential all x.

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And remember if you're not sure what he XP means you can go shift tab.

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Read the docs string here what it says it is and if it's still not clear you can look up the documentation.

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There we go.

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Well it's kind of like a line but a scatter plot is just another word for lots of dots on the plot.

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These are really close together these dots so they kind of look like a line but they are all just dots.

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All right.

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Well how about another set upon let's make another one.

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Just so we can see fig acts.

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I want you to get really used to typing out this thing X P L T dot subplots this is gonna be our bread

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and butter from that plot lib acts dot scatter x let's create a wave because we did one of those before

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and that was my joke.

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Beautiful.

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So we got a nice little wife here and if you're not sure what this is.

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Well this is a sine wave.

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And again you can always see the dock string or look it up.

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No this stamps you can search it off if you're still not sure after looking that use a num ply arrays

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and that's what this section is making figures an umpire raise how about we create a plot directly from

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a dictionary.

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So make a plot from a dictionary because this is just showing how versatile a flexible map plop live

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is.

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So if we want my arm and by the stall we'll bring that back.

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So we want not by the prices.

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That's my favorite food.

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Do you like it.

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I like it too.

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So we want here one by 10.

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It's quite expensive.

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Peanut butter.

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Not as expensive.

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Where I live.

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So aid and cash about is the most expensive cashews are hard to come by you know in short supply sometimes.

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So if I go here and then we've got fig X equals BLT or subplots we're getting really good at this one

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and now we want let's do a bar plot this time.

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So yeah I the trend we're changing this now I know it's kind of confusing because there's a line plot

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but it says plot doesn't say line you're just gonna have to get used to the fact that the default map

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plot lib is a line plot but now we're going to create a bar plot so acts dot bar and we're going to

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pass it x and y so X maybe we'll be a bit more formal X equals nut butter prices what do we want we

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want the keys from our dictionary because we're using a dictionary now not known by array so you want

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the keys here and our y value so we'll type in y that's what plot or bar or scatter always take X and

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Y might put that a little comment x y not but our prices dot values beautiful and now we might see what

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this looks like huh era.

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Missing argument.

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Right.

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Huh that's interesting.

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I see this.

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This one doesn't take why that's a problem positional argument.

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Keyword found.

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What if we get rid of this.

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My bad.

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This doesn't take X and Y see you're learning something new every day shift type x is x x equals that

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and so we can't have we can't have that equaling that.

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So we're going to leave this at that act stop our shift tab.

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So this is a little Python thing if you haven't seen this before it's a syntax error where in a python

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method if you have a positional argument say x equals this you need something after it to go like say

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Y equals this you can't have X equals something and then followed by that but you can have it the other

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way round so see how we don't have x equals.

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We can't have this bean height equals it'll come out the same thing.

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So the first one can't have positional argument where the second one doesn't.

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But the second one can and the first one doesn't have to.

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That's a little bit of a python tidbit there.

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So getting a lot of value from this map plot.

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He likes it here.

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Now this doesn't really make sense.

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We've got a few bars here.

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You can kind of infer that that's prices.

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That's customize it.

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Let's add a little bit of a title may want and not about our prices store and see that little exploration

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that we just did before.

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That's gonna happen right.

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You're gonna make mistakes you're gonna think you know things like me and then you're gonna go oh that's

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actually not like that and then you look at the dock string you'll fix it up and you'll combine your

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previous knowledge and go oh that's why that's like that.

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So don't worry if it happens to you it happens to me.

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Why label your price.

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But what we do is we strive forward and we figure things out shift into.

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There we go.

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That looks a bit better.

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And what can we do to remove that ugly line of text there.

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I believe we can do X not show.

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Nope.

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Well that has no attribute called show but we can add a semicolon up.

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Yeah I believe or maybe down here yeah okay there we go.

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So we're learning on the fly here.

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If you want to remove that nasty line of output code you can add a semicolon to the last line where

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you call ax.

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We've seen this semicolon before but we've just seen where you actually have to put it.

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It's on the last line where you reference ax.

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There we go.

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You can create your own store.

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We've just seen had a plot from num PI a raise.

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We'll look at a little bit more in the next video but to prevent this one from getting too long.

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We'll pause it here.

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So take a little break.

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Create your own plot from a dictionary or a nun pie array and I'll see you shortly.