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The chart... the myth... the Legend. Without adding legends and labels to a chart, the data can appear mythical. Let's see how easy it is to add these in matplotlib.
When we left off, we had a couple
of lines plotted on our chart, but
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no reference as to what either line was.
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This is where legends can come in handy.
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Fortunately, matplotlib makes
this easy to implement as well.
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We add labels to our data series and
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call the legend method on the plot
object before we show it.
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So our label,
we'll just call this dash-dot,
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And dashed, and then call
a legend method and run our cell.
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And there we have a nice legend for
our chart.
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There are several options available for
the legend.
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We can change the location and
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styling on the legend with some
attributes in the legend method.
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I won't go into all those right now, but
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we'll see some of them implemented
throughout this course.
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I'll put a link in the teacher's notes for
more information as well.
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One thing I do want to show you
here is the idea of subplots.
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This allows for multiple plots to
be displayed in the same window.
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We use the subplot method on our plot
object, and pass in the number of rows,
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columns and which panel a particular
plot is going to reside in.
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Let's try this in a new notebook cell.
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So we'll create the first panel.
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Plot subplot, again, rows, columns.
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So two rows, one column, and
this is the first panel.
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We'll use the same x and y values,
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And 16 color, we'll keep as green.
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And we'll keep the first
panel to be dashdot.
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And for the second panel, We want,
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again, two rows, one column,
and this is the second panel.
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Two, three, four and five.
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Let's use that same hex value, 2B5B84
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And call the show method,
and run our cell.
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Very nice, there's one thing to
consider with this image though and
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that's chart scale.
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Both images look like
they have the same slope,
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and hereby represent themselves
as being of similar importance.
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However, when we look at
the numeric values of their axis,
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we see they are very different.
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Let's update the scale of out
two plots here to make their
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axis have the same values.
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We do that by setting limits on our x and
y axis with the set_xlim and
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set_ylim methods.
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We pass in a list of the starting and
ending values for our axes,
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this constrains our charts to those
dimensions along the x and, or y axis.
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Let's see this in action.
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Let's make this a variable.
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Save some typing,
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panel_1 will set xlim 0 to 6.
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Ylim 0 and 20.
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So again, the x limit will now be between
0 and 6 and the y limit will be 0 to 20.
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Let's do this same thing for second panel.
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Panel_2,
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0 and 6.
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0 and 20.
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And run our cell.
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Now that we've normalized the scale of
the plot, we see that the slope of line in
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the upper plot is actually much
steeper than that of the lower line.
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We'll continue to discuss
scale throughout the course.
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It's an important aspect of
data [INAUDIBLE] reporting.
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With some matplotlib basics down,
I think this is a great place for a break.
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Next, we'll discuss some of the chart
options available and why and
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when we would use each one.
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