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Scatter Plot
7:17Let’s utilize a scatter plot to see what correlations if any, there are between the sepal length and width based on the variety of iris.
We left off with our data in a list,
just waiting to be used.
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Let's utilize a scatter plot to
see what correlations, if any,
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there are between the sepal length and
width, based on the variety of aggregates.
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[SOUND] Recall that scatter plots are used
to show how much one variable is impacted
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by another, or its correlation.
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We use scatter plots to show
relationships between values.
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In this case, our sepal length and width.
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The scatter plot allows us to quickly
visualize the distribution of the data and
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notice any outliers.
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We can see if there’s a positive,
negative, or
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nonexistent correlation between our
data based on the scatter plot results.
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Let's jump back into our Python code and
develop our chart.
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Let's get started from
the previous video's code and
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rename name this notebook iris_scatter.
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Since we'll be starting
our work with plots now,
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we'll need to have our matplotlib.pyplot
import to our project.
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matplotlib.pyplot as plt.
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Let's create a dict for
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our marker colors that we can use as
we loop through our list information.
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The colors allow us to see the different
iris classes more easily, and
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visualize a third
variable in our data set.
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I'll paste that dict in here.
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I have included a copy of
it in the teacher's notes.
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Our colors then are the blue hue for
setosa.
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Green, that short code, for versicolor.
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And purple for virginica.
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Our list of iris data also includes an
extra item that we don't need at the end,
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so let's pop that off.
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Now we'll want to loop through our array,
and assign our x and
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y-values to the sepal length and width.
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These are located in the first and
second columns of our array, respectively.
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We can use a function in the itertools
library called groupby that allows us to
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easily do that.
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Let's add that import first and
I'll show you the code.
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From itertools import groupby.
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If you haven't used itertools,
it is a module that provides functions for
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efficient looping.
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Check the teacher's notes for
additional information.
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So to start this, species and
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group in groupby,
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Group is a generator, so
you can only go over it one time.
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And then we'll get our sepal length.
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It's gonna be the float value.
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Sepal widths, similar.
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Then we assign that to plt.scatter.
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Sepal_lengths, sepal_widths for
our y-value there.
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We'll assign it a marker size of 10.
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C for the colors will come from our
colors dict, and grab the species.
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And we'll label based on species as well.
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Now, before we call plt.show,
let's add a plot title, axes labels,
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and legend to our chart here
to add context to our data.
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This is an important thing to remember.
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Always label your axes,
legends, and charts.
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Plt.title, Fisher's Iris Data Set.
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We'll give that a fontsize of 12.
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Bring that up a little bit.
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Our xlabel.
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These are our sepal
lengths in centimeters.
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We’ll assign that a fontsize of 10.
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For our ylabel,
these are our sepal widths.
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Again, in centimeters, and
we'll give that a fontsize of 10 as well.
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We'll call plt.legend, And
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we'll give this a location
in the upper right.
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Here we are setting the legend location
to be displayed in the upper right
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of the chart.
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But we could display it in the upper left,
upper center, bottom left, etc.
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Since there aren't any data points
being displayed in the upper right,
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that seems like a good position.
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Now we just call plt.show.
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And run our cell.
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We can see some patterns
here in our sepal data.
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Iris-setosa is a pretty good grouping in
the upper left quadrant of our chart.
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There are some outliers though.
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The other two varieties seem
to be clumped together and
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intermixed with some
even greater outliers.
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Our plot looks a bit small here, though.
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Let's assign a size to our figure
to make it a bit easier to see.
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We do that, Go up here,
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right under input_file,
that's kind of a standard spot for it.
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We attach something to the figure object,
figsize.
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7.5, 4.25 seems to work pretty well,
and we can run our cell again.
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There, that's better.
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From an analysis standpoint, we could draw
some conclusions based on this chart.
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It appears that all three iris
varieties have a positive correlation
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between sepal length and width.
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Iris-setosa has a better defined positive
correlation than the other varieties.
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Scatter plots are, of course,
only one way to explore our data.
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