Plotting Line Plots

Quick reference

• 00:05 Okay, in the last video, we looked at bar plots.
• 00:07 In this video, we want to look at line plots.
• 00:09 So, line plots are fairly simple,
• 00:12 we could just call my_df.plot() and we want kind to equal line.
• 00:17 And if we just run this, we get this crazy jumbled thing.
• 00:22 And just like earlier, we can do an alpha and
• 00:26 set this equal to some transparent thing.
• 00:30 If that's better or worse.
• 00:32 Now we can change the size of this is,
• 00:33 kind of jumbled together, kind of hard to see all this.
• 00:36 So we can go figsize, =,
• 00:40 and then pass in two more parentheses, and then just designate how big we want this.
• 00:44 So let's say 10 by 5.
• 00:46 It's slightly bigger.
• 00:49 We could go 20 by anything you want, really.
• 00:54 So it's much bigger, and it's pretty cool.
• 00:57 Now, line plots, you can designate the axis x and y.
• 01:02 So if you come down here and
• 01:03 look, we see this is the label axis, sort of the position.
• 01:07 The default is none, right?
• 01:09 You can also have y which is a label position or list of label positions, and
• 01:13 the default is none.
• 01:15 So if you need to switch these things around or anything like that,
• 01:17 you can just do that by passing x = or y = right here.
• 01:22 So here we're plotting everything from our data frame.
• 01:25 It's kind of messy, we might just want Monday.
• 01:30 And that's pretty big.
• 01:31 Let's change this back to 10 and 5.
• 01:37 Right?
• 01:38 If we want more than one column,
• 01:41 we could just pass in another set of square brackets and
• 01:45 say if we want Monday and Wednesday, boom, you get this.
• 01:50 And like everything else, we can pass in a title
• 01:55 if we want, a line plot, with a nice title.
• 02:00 We can remove our legend if we want.
• 02:02 Set that equal to false.
• 02:05 Now it's gone, and very cool.
• 02:08 So let's change this back.
• 02:10 We get this crazy jumbled thing.
• 02:16 So we're using this dot plot function with passing the kind argument in.
• 02:20 Or like other things, we can just call my_df.plot.line and
• 02:26 then pass in whatever you want, right?
• 02:30 But again, it's just a personal preference.
• 02:32 I kind of like passing kind = line, just makes it easier for
• 02:36 me to remember these things.
• 02:38 So, let's see, what else can we do?
• 02:39 We can also change the line, sort of width, right?
• 02:42 So we could go lw, line width, =, for instance, 5.
• 02:46 We want to make this really kind of big and chunky.
• 02:49 And we can go 10 if we want.
• 02:51 If we want to get ridiculous,
• 02:52 looks like somebody used a crayon on this thing, right?
• 02:55 You can bring it back down to 1 if you want to, to do it even skinnier.
• 02:59 So without that, let's just run this again.
• 03:06 You can see, yeah, that's a little bit skinnier than the default, right?
• 03:11 So pretty cool.
• 03:12 In fact, you can use all the map plot Libby type things inside of these
• 03:17 because all of the plotting we're doing is built on top of Matplotlib.
• 03:21 So if you know Matplotlib and the different things you can do with
• 03:25 Matplotlib, you can do most of those things to all of these charts and
• 03:29 graphs that we're creating.
• 03:31 So that's line plots, pretty straightforward.
• 03:33 In the next video, we'll look at scatter plots.

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