matplotlib: plotting histogram plot just above scatter plot

Question

I would like to make beautiful scatter plots with histograms above and right of the scatter plot, as it is possible in seaborn with jointplot:

I am looking for suggestions on how to achieve this. In fact I am having some troubles in installing pandas, and also I do not need the entire seaborn module

Answer 1

Here's an example of how to do it, using gridspec.GridSpec:

import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import numpy as np

x = np.random.rand(50)
y = np.random.rand(50)

fig = plt.figure()

gs = GridSpec(4,4)

ax_joint = fig.add_subplot(gs[1:4,0:3])
ax_marg_x = fig.add_subplot(gs[0,0:3])
ax_marg_y = fig.add_subplot(gs[1:4,3])

ax_joint.scatter(x,y)
ax_marg_x.hist(x)
ax_marg_y.hist(y,orientation="horizontal")

# Turn off tick labels on marginals
plt.setp(ax_marg_x.get_xticklabels(), visible=False)
plt.setp(ax_marg_y.get_yticklabels(), visible=False)

# Set labels on joint
ax_joint.set_xlabel('Joint x label')
ax_joint.set_ylabel('Joint y label')

# Set labels on marginals
ax_marg_y.set_xlabel('Marginal x label')
ax_marg_x.set_ylabel('Marginal y label')
plt.show()

Answer 2

I encountered the same problem today. Additionally I wanted a CDF for the marginals.

Code:

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import numpy as np

x = np.random.beta(2,5,size=int(1e4))
y = np.random.randn(int(1e4))

fig = plt.figure(figsize=(8,8))
gs = gridspec.GridSpec(3, 3)
ax_main = plt.subplot(gs[1:3, :2])
ax_xDist = plt.subplot(gs[0, :2],sharex=ax_main)
ax_yDist = plt.subplot(gs[1:3, 2],sharey=ax_main)

ax_main.scatter(x,y,marker='.')
ax_main.set(xlabel="x data", ylabel="y data")

ax_xDist.hist(x,bins=100,align='mid')
ax_xDist.set(ylabel='count')
ax_xCumDist = ax_xDist.twinx()
ax_xCumDist.hist(x,bins=100,cumulative=True,histtype='step',normed=True,color='r',align='mid')
ax_xCumDist.tick_params('y', colors='r')
ax_xCumDist.set_ylabel('cumulative',color='r')

ax_yDist.hist(y,bins=100,orientation='horizontal',align='mid')
ax_yDist.set(xlabel='count')
ax_yCumDist = ax_yDist.twiny()
ax_yCumDist.hist(y,bins=100,cumulative=True,histtype='step',normed=True,color='r',align='mid',orientation='horizontal')
ax_yCumDist.tick_params('x', colors='r')
ax_yCumDist.set_xlabel('cumulative',color='r')

plt.show()

Hope it helps the next person searching for scatter-plot with marginal distribution.