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About this lesson

In this video, we discuss density plots, kernel density estimation (KDE) plots, and how to create them.

Exercise files

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Plotting Density Plots.docx
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Quick reference

Plotting Density Plots

Kernel Density Plots, and Density plots, are basically the same thing and allow you to estimate the probability density function of a random variable.

When to use

Use the methods whenever you want to chart the kernel density of your data.

Instructions

The first method of creating a kernel density plot is:
   my_df.plot(kind='kde')

The second method of creating a kernal density plot is:
   my_df.plot.kde()

You need scipy to run a kde plot. From inside the terminal, run:
   pip install scipy

 

Hints & tips

  • my_df.plot(kind='kde')
  • my_df.plot.kde()
  • pip install scipy
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  • 00:05 Okay, in the last video, we looked at hex plots.
  • 00:07 In this video, we want to look at density plots and kernel density estimation plots.
  • 00:12 Basically the same thing, although slightly different.
  • 00:14 So let's just jump right in here.
  • 00:17 Let's go my_df.plot.
  • 00:20 And for kind we want, let's start out with kernel density,
  • 00:26 so kde, kernel density estimation.
  • 00:29 And if we run this, we're going to get an error.
  • 00:31 So if we come down here and look, we see, No module named scipy.
  • 00:34 So we actually need to install SciPy, Scientific Python, in order to use this.
  • 00:40 So we can do that very quickly.
  • 00:42 Just head back over to our terminal,
  • 00:44 Ctrl+C to break out of the Jupyter Notebook.
  • 00:47 And we can just pip install scypy, all one word,
  • 00:52 and it should just take a second.
  • 00:55 And now we can run our juptyter notebook again, and it pops back on.
  • 01:03 So I'm just going to close this.
  • 01:04 Now we can reload our current notebook, and we're still in our plot notebook.
  • 01:09 And we're going to need to come up here and Shift+Enter to run these three fields
  • 01:13 again, just to get this stuff in the current memory,
  • 01:17 since we had to restart our notebook.
  • 01:19 And now if we come down here, my_df.plot,
  • 01:24 and then, kind='kde'.
  • 01:28 And now we get a kernel density estimation for each of our columns,
  • 01:33 Monday, Tuesday, Wednesday, and Thursday.
  • 01:36 And a KDE is sort of like a histogram, but it's not quite the same.
  • 01:41 In fact, we can come down here and we can go my_df.plot.kde.
  • 01:47 And then Shift+Enter, to see exactly what the documentation says.
  • 01:52 And in statistics, kernel density estimation, KDE, is a nonparametric way to
  • 01:56 estimate the probability density function, PDF, of a random variable.
  • 02:01 The function uses Gaussian kernels and
  • 02:03 includes automatic bandwidth determination.
  • 02:06 And we come through here, there's not much really to see.
  • 02:07 It says some SciPy stuff, and really not much to learn there.
  • 02:15 But we can do some different things here.
  • 02:18 We can call alpha=0.4.
  • 02:22 I'll spell alpha right.
  • 02:24 To make these a little bit transparent if we want to see them better or
  • 02:31 whatever, we can do line width equals 2, or
  • 02:36 5 to make them fatter if we want, and all the things.
  • 02:41 And just like KDE, we can also do a density plot,
  • 02:48 And it's slightly different here.
  • 02:50 And we can come down here, so my_df.plot.density, and run this.
  • 02:57 We can Shift+Tab to read about this if we want to.
  • 03:02 And we get the same kernel density estimation spiel.
  • 03:07 These are basically the same thing.
  • 03:09 Okay, so we went through a lot of these plots very, very quickly, and
  • 03:13 we didn't get into great detail about them.
  • 03:15 But that's not really what I wanted to do in this course.
  • 03:18 I don't want to give you a great detail.
  • 03:20 I just want to show you what's available and sort of introduce you to
  • 03:24 each of these things, all of these plots, sort of slowly.
  • 03:27 Because in data analysis with Python, it's easy to get overwhelmed.
  • 03:31 So I really just want you to become familiar with all these plots.
  • 03:34 Play around with them a little bit, get used to sort of the attributes that you
  • 03:38 can play with, changing the line width, changing the colors,
  • 03:41 just passing basic data into them, seeing the results.
  • 03:44 because in the future, you'll learn how to use them in more advanced ways.
  • 03:46 But first, you have to become familiar with them.
  • 03:48 And that's really what I want you to get out of this course,
  • 03:51 just a basic familiarity with the different plotting that you can
  • 03:54 do with pandas using these visualization techniques of basic plots.
  • 03:59 So we looked at histograms, we looked at area plots.
  • 04:01 We looked at bar plots, line plots, scatter plots, box plots,
  • 04:05 I love the box plots, hex plots, and density plots.
  • 04:08 And you've got a nice little toolkit now to work from going forward.
  • 04:12 So that's all for this section.
  • 04:14 In the next section, we're going to start to look at linear regression with
  • 04:18 scikit-learn, talk about what linear regression is, how to train models and
  • 04:22 do fun stuff like that.
  • 04:24 And that'll be starting in the next video.

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