Let's say that I don't like the default choices of the axes of this value in the previous graph. It's automatically fitting it to the tightest set of the axis values that you can find, which is usually a good thing to do, but sometimes you want things on an absolute scale. Look at the following code:

axes = plt.axes() 
axes.set_xlim([-5, 5]) 
axes.set_ylim([0, 1.0]) 
axes.set_xticks([-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5]) 
axes.set_yticks([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]) 
plt.plot(x, norm.pdf(x)) 
plt.plot(x, norm.pdf(x, 1.0, 0.5)) 
plt.show() 

In this example, first I get the axes using plt.axes. Once I have these axes objects, I can adjust them. By calling set_xlim, I can set the x range from -5 to 5 and with set set_ylim, I set the y range from 0 to 1. You can see in the below output, that my x values are ranging from -5 to 5, and y goes from 0 to 1. I can also have explicit control over where the tick marks on the axes are. So in the previous code, I'm saying I want the x ticks to be at -5, -4, - 3, etc., and y ticks from 0 to 1 at 0.1 increments using the set_xticks() and set_yticks() functions. Now I could use the arange function to do that more compactly, but the point is you have explicit control over where exactly those tick marks happen, and you can also skip some. You can have them at whatever increments you want or whatever distribution you want. Beyond that, it's the same thing.

Once I've adjusted my axes, I just called plot() with the functions that I want to plot and called show() to display it. Sure enough, there you have the result.