04-using-hist2d.py

'''
Using hist2d()

Given a set of ordered pairs describing data points, you can count the number of points with similar values to construct a two-dimensional histogram. This is similar to a one-dimensional histogram, but it describes the joint variation of two random variables rather than just one.

In matplotlib, one function to visualize 2-D histograms is plt.hist2d().

You specify the coordinates of the points using plt.hist2d(x,y) assuming x and y are two vectors of the same length.
You can specify the number of bins with the argument bins=(nx, ny) where nx is the number of bins to use in the horizontal direction and ny is the number of bins to use in the vertical direction.
You can specify the rectangular region in which the samples are counted in constructing the 2D histogram. The optional parameter required is range=((xmin, xmax), (ymin, ymax)) where
xmin and xmax are the respective lower and upper limits for the variables on the x-axis and
ymin and ymax are the respective lower and upper limits for the variables on the y-axis. Notice that the optional range argument can use nested tuples or lists.
In this exercise, you'll use some data from the auto-mpg data set. There are two arrays mpg and hp that respectively contain miles per gallon and horse power ratings from over three hundred automobiles built.

INSTRUCTIONS
100XP
Generate a two-dimensional histogram to view the joint variation of the mpg and hp arrays.
Put hp along the horizontal axis and mpg along the vertical axis.
Specify 20 by 20 rectangular bins with the bins argument.
Specify the region covered with the optional range argument so that the plot samples hp between 40 and 235 on the x-axis and mpg between 8 and 48 on the y-axis.
Add a color bar to the histogram.
'''
# Generate a 2-D histogram
plt.hist2d(hp,mpg, bins=(20,20), range=((40, 235), (8,48)))

# Add a color bar to the histogram
plt.colorbar()

# Add labels, title, and display the plot
plt.xlabel('Horse power [hp]')
plt.ylabel('Miles per gallon [mpg]')
plt.title('hist2d() plot')
plt.show()