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================ by Jawad Haider

Chpt 4 - Visualization with Matplotlib

09 - Multiple Subplots

Multiple Subplots

Sometimes it is helpful to compare different views of data side by side. To this end, Matplotlib has the concept of subplots: groups of smaller axes that can exist together within a single figure. These subplots might be insets, grids of plots, or other more complicated layouts. In this section, we’ll explore four routines for creating subplots in Matplotlib. We’ll start by setting up the notebook for plotting and importing the functions we will use:

%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-white')
import numpy as np

plt.axes: Subplots by Hand

The most basic method of creating an axes is to use the plt.axes function. As we’ve seen previously, by default this creates a standard axes object that fills the entire fig‐ ure. plt.axes also takes an optional argument that is a list of four numbers in the figure coordinate system. These numbers represent [bottom, left, width, height] in the figure coordinate system, which ranges from 0 at the bottom left of the figure to 1 at the top right of the figure.

ax1=plt.axes()
ax2=plt.axes([0.65, 0.65, 0.2,0.2])

The equivalent of this command within the object-oriented interface is fig.add_axes(). Let’s use this to create two vertically stacked axes

fig = plt.figure()
ax1=fig.add_axes([0.1,0.5, 0.8,0.4],
                xticklabels=[],ylim=(-1.2,1.2))
ax2=fig.add_axes([0.1,0.1
                  , 0.8,0.4],
                xticklabels=[],ylim=(-1.2,1.2))

x=np.linspace(-0,10)
ax1.plot(np.sin(x))
ax2.plot(np.cos(x));

plt.subplot: Simple Grids of Subplots

Aligned columns or rows of subplots are a common enough need that Matplotlib has several convenience routines that make them easy to create. The lowest level of these is plt.subplot(), which creates a single subplot within a grid. As you can see, this command takes three integer arguments—the number of rows, the number of col‐ umns, and the index of the plot to be created in this scheme, which runs from the upper left to the bottom right

for i in range(1,7):
    plt.subplot(2,3,i)
    plt.text(0.5,0.5,str((2,3,i)),
            fontsize=18,ha='center')

The command plt.subplots_adjust can be used to adjust the spacing between these plots.

object-oriented command, fig.add_subplot():

fig = plt.figure()
fig.subplots_adjust(hspace=0.4,wspace=0.4)
for i in range(1,7):
    ax=fig.add_subplot(2,3,i)
    ax.text(0.5,0.5, str((2,3,i)),
           fontsize=18, ha='center')

plt.subplots: The Whole Grid in One Go

The approach just described can become quite tedious when you’re creating a large grid of subplots, especially if you’d like to hide the x- and y-axis labels on the inner plots. For this purpose, plt.subplots() is the easier tool to use (note the s at the end of subplots). Rather than creating a single subplot, this function creates a full grid of subplots in a single line, returning them in a NumPy array. The arguments are the number of rows and number of columns, along with optional keywords sharex and sharey, which allow you to specify the relationships between different axes.

fig,ax=plt.subplots(2,3,sharex='col',sharey='row')


for i in range(2):
    for j in range(3):
        ax[i,j].text(0.5,0.5, str((2,3,i)),
           fontsize=18, ha='center')
fig

plt.GridSpec: More Complicated Arrangements

To go beyond a regular grid to subplots that span multiple rows and columns, plt.GridSpec() is the best tool. The plt.GridSpec() object does not create a plot by itself; it is simply a convenient interface that is recognized by the plt.subplot() command.

grid=plt.GridSpec(2,3,wspace=0.4, hspace=0.3)

plt.subplot(grid[0,0])
plt.subplot(grid[0,1:])
plt.subplot(grid[1,:2])
plt.subplot(grid[1,2]);


# Create some normally distributed data
mean=[0,0]
cov=[[1,1],[1,2]]
x,y=np.random.multivariate_normal(mean,cov,3000).T

# Set up the axes with gridspec
fig = plt.figure(figsize=(6,6))
grid=plt.GridSpec(4,4, wspace=0.2, hspace=0.2)
main_ax=fig.add_subplot(grid[:-1,1:])
y_hist=fig.add_subplot(grid[:-1,0],xticklabels=[], sharey=main_ax)
x_hist=fig.add_subplot(grid[-1,1:], yticklabels=[], sharex=main_ax)

# scatter points on the main axes
main_ax.plot(x,y,'ok',markersize=3,alpha=0.2)

# historgram on the attached axes
x_hist.hist(x, 40, histtype='stepfilled', 
           orientation='vertical', color='gray')
x_hist.invert_yaxis()
y_hist.hist(y, 40, histtype='stepfilled',
           orientation='horizontal', color='gray')
y_hist.invert_xaxis()