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Histogram
6:01Histograms are used to show distributions of data. Let's explore the Iris data set with this chart style.
As I've mentioned, histograms are used
to show distributions of data.
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This can be very useful to see
how closely grouped together or
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spread out a variable is.
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The area of the rectangles in
a histogram is proportional
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to the frequency of the variable.
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This allows for
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the rough assessment of the probable
distribution of a given variable.
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The rectangles or
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bins in a histogram, are important to
consider when doing data visualization.
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Both the number of overall bins and
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the bin width can have an impact on
the overall presentation of data.
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From our iris data set let's generate
a histogram chart to see the distribution
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of petal length.
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Let's examine the petal lengths
of the iris virginica class and
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visualize the distribution of that data.
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Here's where we start off in
a new notebook, iris histogram,
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with bringing in our data and
getting it stored in a list called irises.
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Let's process through this list
to just obtain the petal length
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of the iris virginica species.
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Create a list, hold our data.
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Let's also create a variable for
our bin numbers,
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so we can see how changing bin
numbers impacts our visualization.
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Now let's loop through our
data to get our petal lengths.
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For petal in range of our iris data.
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So if the species is Iris-virginica,
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we'll add the petal length to
our virginica_petal_length list.
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And we'll get that from our iris data.
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Now we can pass our data
into our plot.hist method.
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This method takes several parameters,
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including the number of
bins we'd like to have.
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The color we'd like to set,
along with alpha values.
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plt.hist pass in our
virginica_petal_length.
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Our number bins.
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The color of our plot will be red.
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And we give it an alpha value
to make it slightly transparent.
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As I've mentioned, it's always
important to add labels to your charts.
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For chart title.
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Iris-virginica Petal length.
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We'll give that a font size of 12.
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For our x-axis, for xlabel,
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we'll give it what it is,
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Petal length in centimeters.
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Font size of 10.
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And for our ylabel.
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We'll just call it Probability.
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And again,
we'll give that a font size of 10.
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Cool and then we call our show method and
run our cell.
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We are shown a histogram
chart with red rectangles.
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However, the rectangles
are all clumped together and
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can be a challenge to differentiate.
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Matplotlib allows for and includes some
chart styling options which can help out.
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Let's apply matplotlib's
classic style to our chart and
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see if it helps clear things up.
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We'll go back up here and
under where we assign our figure size.
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We'll ask it to use the classic style and
then we can run our cell.
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That's much better.
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Now we are setting our
number of bins to ten,
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which is also the matplotlib default for
histograms.
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Let's change that the 15 and then to 5 to
see how that impacts our visualization.
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Notice here that at 15 bins
we have some empty bins.
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While we get more detail about the data
set, it also spreads the data into
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a broken comb look that doesn't provide as
clear of a picture of the distribution.
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And if we go back and set it to 5 bins.
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At 5 bins,
the data isn't portrayed very well either.
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There are a variety of formulas and
considerations for the number of bins and
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their widths to use.
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I've included links to some resources for
these in the teacher's notes.
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It is not uncommon in practice
to produce multiple histograms
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with different numbers of bins, before
settling on the best communication tool.
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Histograms are great for
exploring the distribution of data, but
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our data set has many more
ways that it can be explored.
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Sepal length and sepal and
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pedal width, can all be explored
across all different species.
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Before the next video,
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practice creating some other
histograms of this data on your own.
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Next, we'll look at box plots.
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