The Legend of Charting5:25 with Ken Alger
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 0:00 no reference as to what either line was. 0:03 This is where legends can come in handy. 0:06 Fortunately, matplotlib makes this easy to implement as well. 0:09 We add labels to our data series and 0:14 call the legend method on the plot object before we show it. 0:16 So our label, we'll just call this dash-dot, 0:23 And dashed, and then call a legend method and run our cell. 0:31 And there we have a nice legend for our chart. 0:41 There are several options available for the legend. 0:44 We can change the location and 0:47 styling on the legend with some attributes in the legend method. 0:48 I won't go into all those right now, but 0:53 we'll see some of them implemented throughout this course. 0:55 I'll put a link in the teacher's notes for more information as well. 0:58 One thing I do want to show you here is the idea of subplots. 1:02 This allows for multiple plots to be displayed in the same window. 1:06 We use the subplot method on our plot object, and pass in the number of rows, 1:09 columns and which panel a particular plot is going to reside in. 1:14 Let's try this in a new notebook cell. 1:18 So we'll create the first panel. 1:29 Plot subplot, again, rows, columns. 1:34 So two rows, one column, and this is the first panel. 1:37 We'll use the same x and y values, 1:46 And 16 color, we'll keep as green. 1:54 And we'll keep the first panel to be dashdot. 2:02 And for the second panel, We want, 2:13 again, two rows, one column, and this is the second panel. 2:18 Two, three, four and five. 2:31 Let's use that same hex value, 2B5B84 2:37 And call the show method, and run our cell. 2:51 Very nice, there's one thing to consider with this image though and 2:58 that's chart scale. 3:02 Both images look like they have the same slope, 3:04 and hereby represent themselves as being of similar importance. 3:07 However, when we look at the numeric values of their axis, 3:12 we see they are very different. 3:16 Let's update the scale of out two plots here to make their 3:18 axis have the same values. 3:21 We do that by setting limits on our x and y axis with the set_xlim and 3:23 set_ylim methods. 3:28 We pass in a list of the starting and ending values for our axes, 3:31 this constrains our charts to those dimensions along the x and, or y axis. 3:35 Let's see this in action. 3:41 Let's make this a variable. 3:45 Save some typing, 3:49 panel_1 will set xlim 0 to 6. 3:54 Ylim 0 and 20. 4:08 So again, the x limit will now be between 0 and 6 and the y limit will be 0 to 20. 4:12 Let's do this same thing for second panel. 4:21 Panel_2, 4:28 0 and 6. 4:38 0 and 20. 4:45 And run our cell. 4:52 Now that we've normalized the scale of the plot, we see that the slope of line in 4:57 the upper plot is actually much steeper than that of the lower line. 5:02 We'll continue to discuss scale throughout the course. 5:07 It's an important aspect of data [INAUDIBLE] reporting. 5:11 With some matplotlib basics down, I think this is a great place for a break. 5:14 Next, we'll discuss some of the chart options available and why and 5:19 when we would use each one. 5:23
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