Your data is only as good as you can present it to other people, really, so let's talk about plotting and graphing your data and how to present it to others and make your graphs look pretty. We're going to introduce Matplotlib more thoroughly and put it through its paces.

I'll show you a few tricks on how to make your graphs as pretty as you can. Let's have some fun with graphs. It's always good to make pretty pictures out of your work. This will give you some more tools in your tool chest for visualizing different types of data using different types of graphs and making it look pretty. We'll use different colors, different line styles, different axes, things like that. It's not only important to use graphs and data visualization to try to find interesting patterns in your data, but it's also interesting to present your findings well to a non-technical audience. Without further ado, let's dive in to Matplotlib.

Go ahead and open up the MatPlotLib.ipynb file and you can play around with this stuff along with me. We'll start by just drawing a simple line graph.

%matplotlib inline 
 
from scipy.stats import norm 
import matplotlib.pyplot as plt 
import numpy as np 

x = np.arange(-3, 3, 0.001) 
 
plt.plot(x, norm.pdf(x)) 
plt.show() 

So in this example, I import matplotlib.pyplot as plt, and with this, we can refer to it as plt from now on in this notebook. Then, I use np.arange(-3, 3, 0.001) to create an x-axis filled with values between -3 and 3 at increments of 0.001, and use pyplot's plot() function to plot x. The y function will be norm.pdf(x). So I'm going to create a probability density function with a normal distribution based on the x values, and I'm using the scipy.stats norm package to do that.

So tying it back into last chapter's look at probability density functions, here we are plotting a normal probability density function using matplotlib. So we just call pyplot's plot() method to set up our plot, and then we display it using plt.show(). When we run the previous code, we get the following output:

That's what we get: a pretty little graph with all the default formatting.