plt.grid(True)
plt.legend(loc= 0 )
plt.axis(‘tight’)
plt.xlabel(‘index’)
plt.ylabel(‘value’)
Figure 5-11. Plot with two subplots
The placing of subplots in the a matplotlib figure object is accomplished here by the
use of a special coordinate system. plt.subplot takes as arguments three integers for
numrows, numcols, and fignum (either separated by commas or not). numrows specifies the
number of rows, numcols the number of columns, and fignum the number of the sub-plot,
starting with 1 and ending with numrows * numcols. For example, a figure with nine
equally sized subplots would have numrows=3, numcols=3, and fignum=1,2,...,9. The
lower-right subplot would have the following “coordinates”: plt.subplot(3, 3, 9).
Sometimes, it might be necessary or desired to choose two different plot types to visualize
such data. With the subplot approach you have the freedom to combine arbitrary kinds of
plots that matplotlib offers.
[ 23 ]
Figure 5-12 combines a line/point plot with a bar chart:
In [ 15 ]: plt.figure(figsize=( 9 , 4 ))
plt.subplot( 121 )
plt.plot(y[:, 0 ], lw=1.5, label=‘1st’)
plt.plot(y[:, 0 ], ‘ro’)
plt.grid(True)
plt.legend(loc= 0 )
plt.axis(‘tight’)
plt.xlabel(‘index’)
plt.ylabel(‘value’)
plt.title(‘1st Data Set’)
plt.subplot( 122 )
plt.bar(np.arange(len(y)), y[:, 1 ], width=0.5,
color=‘g’, label=‘2nd’)
plt.grid(True)
plt.legend(loc= 0 )
plt.axis(‘tight’)
plt.xlabel(‘index’)
plt.title(‘2nd Data Set’)
