Python Tutorial: Plotting multiple graphs

Want to learn more? Take the full course at https://learn.datacamp.com/courses/introduction-to-data-visualization-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- We've seen already that the pyplot module from Matplotlib... ...is our workhorse to construct plots quickly. We'll see how to plot graphs on the same axes, how to create additional tailored axes, and how to create a grid of axes (called subplots) within a single figure. To start, let's assume we have three NumPy arrays: t for time temperature, and dewpoint. These come from weather measurements over the course of two weeks in January 2010. Ths first plot() command plots temperature vs. t in red. The arguments to plot() can be NumPy arrays, lists, or Pandas Series. The curve is not actually drawn, just created in memory. The second plot() command issued in sequence actually overlays a curve of dewpoint vs. t in blue on the same axes. The next two lines create a label for the horizontal axis and a title. The last line with the show() command actually displays the figure on screen once the sequence of plot objects from prior instructions have been positioned. This is the resulting figure with two curves overlaid on the same axes. Notice the axis tick labels are messy here; you'll see how to clean those up later. Common axes are convenient for comparing curves directly, but they can get messy. We'll try to draw the same two curves, this time on multiple axes in the same figure; overlaying curves is not useful when the scales differ widely. The tool to construct axes explicitly is the axes() command. We'll explain the numbers shortly; just realize that the axes() command creates coordinate axes within a figure within which subsequent plots are drawn. Thus, the first line constructs axes on the left side of the figure window and the subsequent three lines create a curve, an axis label, and a title within those axes. The