When we left off, we had a couple of lines plotted on our chart, but

no reference as to what either line was.

This is where legends can come in handy.

Fortunately, matplotlib makes this easy to implement as well.

We add labels to our data series and

call the legend method on the plot object before we show it.

So our label, we'll just call this dash-dot,

And dashed, and then call a legend method and run our cell.

And there we have a nice legend for our chart.

There are several options available for the legend.

We can change the location and

styling on the legend with some attributes in the legend method.

I won't go into all those right now, but

we'll see some of them implemented throughout this course.

I'll put a link in the teacher's notes for more information as well.

One thing I do want to show you here is the idea of subplots.

This allows for multiple plots to be displayed in the same window.

We use the subplot method on our plot object, and pass in the number of rows,

columns and which panel a particular plot is going to reside in.

Let's try this in a new notebook cell.

So we'll create the first panel.

Plot subplot, again, rows, columns.

So two rows, one column, and this is the first panel.

We'll use the same x and y values,

And 16 color, we'll keep as green.

And we'll keep the first panel to be dashdot.

And for the second panel, We want,

again, two rows, one column, and this is the second panel.

Two, three, four and five.

Let's use that same hex value, 2B5B84

And call the show method, and run our cell.

Very nice, there's one thing to consider with this image though and

that's chart scale.

Both images look like they have the same slope,

and hereby represent themselves as being of similar importance.

However, when we look at the numeric values of their axis,

we see they are very different.

Let's update the scale of out two plots here to make their

axis have the same values.

We do that by setting limits on our x and y axis with the set_xlim and

set_ylim methods.

We pass in a list of the starting and ending values for our axes,

this constrains our charts to those dimensions along the x and, or y axis.

Let's see this in action.

Let's make this a variable.

Save some typing,

panel_1 will set xlim 0 to 6.

Ylim 0 and 20.

So again, the x limit will now be between 0 and 6 and the y limit will be 0 to 20.

Let's do this same thing for second panel.

Panel_2,

0 and 6.

0 and 20.

And run our cell.

Now that we've normalized the scale of the plot, we see that the slope of line in

the upper plot is actually much steeper than that of the lower line.

We'll continue to discuss scale throughout the course.

It's an important aspect of data [INAUDIBLE] reporting.

With some matplotlib basics down, I think this is a great place for a break.

Next, we'll discuss some of the chart options available and why and

when we would use each one.