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Box plots are useful when visualizing multiple variables simultaneously on the same chart.
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We've explored some very useful ways
of visualizing data with line charts,
0:00
scatter plots and histograms.
0:04
Now, let's take a look at box plots.
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Much like histograms,
they show the distribution of data.
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Box plots are useful when visualizing
multiple variables simultaneously
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on the same chart.
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This allows us to reduce the number
of charts for presentation.
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[SOUND] Each variable shows a box,
or rectangle.
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The bottom of the rectangle represents
the level of the first quartile.
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The top of the box represents
the third quartile of the data set.
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The median is typically represented
by a line inside the box.
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And the minimum and maximum values
are represented by whiskers below and
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above the box respectively.
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If that sounds confusing,
let's jump in and
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see these plots in action
with our iris data.
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We'll start with our opening project
code in a new iris_boxplot notebook.
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And then get the data for
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each of our iris classes, Setosa,
Versicolor, and Virginica.
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For this DataVis exercise, let's examine
the petal length of each iris class.
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We'll pass a Python
list into our box plot.
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So let's create an empty list.
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We'll append values to this list
as we loop through our data.
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Now we'll create our for
loop to generate our list.
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This should be pretty familiar as well,
but
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we're appending the data to our
list based on the iris class.
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For species and group, And groupby,
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And we'll append iris data,
or our petal data.
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And since we're using the groupby method
again here, we'll need that import.
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From itertools, import groupby.
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Now we can pass our
data into our box plot.
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Plt.boxplot, and pass in petal_lengths.
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And call the show method.
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Now while that provides us with
our plotting, let's add some
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polish by setting some axis size
parameters and our plot labels.
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Let's also get rid of that 1,
2, 3, and label those ticks.
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Come up here, we'll set our axis,
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0, 4, 0, 10.
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For the tick labels, we pass in a list of
the ticks with their default values and
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an equal length list of the values
we want them replaced with.
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So xticks, 1, 2, and 3.
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And we're gonna replace those
with the iris names of Setosa,
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Versicolor, And Virginica.
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Now we'll add in our plot title and
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move it to the left instead of
the default center, and label the axis.
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Fisher's Iris Data Set.
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Petal Length.
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Go to fontsize of 12, and
the location is left.
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Our xlabel, Iris Variety.
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We'll do fontsize 10.
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And for the ylabel Petal length,
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In centimeters, and
again we'll do a fontsize of 10 there.
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And now, let's try it out, run our cell.
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And looking at our results here, we see
that Iris Setosa on the left has a much
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tighter distribution of petal length
than Iris Virginica on the right.
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We can also see with the red lines in the
boxes, which are their median values or
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the petal lengths.
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Iris Setosa has a median value around 1.5.
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Versicolor, about 4.25.
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And Virginica,
just a bit over 5.5 centimeters long.
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We've now seen four different charts
that can be used to visualize our data.
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But don't feel boxed in to
using just these charts.
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There are a lot of other
options in Matplotlib.
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Including the ability to display
multiple charts in a single view.
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Let's take a deeper look at how we can
configure our output in the next video.
5:31
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