matplotlib - How to create a heatmap with marginal histograms, similar to a jointplot?

Question

I want to plot 2-dimensional scalar data, which I would usually plot using matplotlib.pyplot.imshow or sns.heatmap. Consider this example:

data = [[10, 20, 30], [50, 50, 100], [80, 60, 10]]
fix, ax = plt.subplots()
ax.imshow(data, cmap=plt.cm.YlGn)

Now I additionally would like to have one-dimonsional bar plots at the top and the right side, showing the sum of the values in each column / row - just as sns.jointplot does. However, sns.jointplot seems only to work with categorical data, producing histograms (with kind='hist'), scatterplots or the like - I don't see how to use it if I want to specify the values of the cells directly. Is such a thing possible with seaborn?

The y axis in my plot is going to be days (within a month), the x axis is going to be hours. My data looks like this:

The field Cost Difference is what should make up the shade of the respective field in the plot.

question from:https://stackoverflow.com/questions/65917235/how-to-create-a-heatmap-with-marginal-histograms-similar-to-a-jointplot

Answer 1

Here is an approach that first creates a dummy jointplot and then uses its axes to add a heatmap and bar plots of the sums.

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd

D = 28
H = 24
df = pd.DataFrame({'day': np.repeat(range(1, D + 1), H),
                   'hour': np.tile(range(H), D),
                   'Cost Dif.': np.random.uniform(10, 1000, D * H)})
# change the random df to have some rows/columns stand out (debugging, checking)
df.loc[df['hour'] == 10, 'Cost Dif.'] = 150
df.loc[df['hour'] == 12, 'Cost Dif.'] = 250
df.loc[df['day'] == 20, 'Cost Dif.'] = 800

g = sns.jointplot(data=df, x='day', y='hour', kind='hist', bins=(D, H))
g.ax_marg_y.cla()
g.ax_marg_x.cla()
sns.heatmap(data=df['Cost Dif.'].to_numpy().reshape(D, H).T, ax=g.ax_joint, cbar=False, cmap='Blues')

g.ax_marg_y.barh(np.arange(0.5, H), df.groupby(['hour'])['Cost Dif.'].sum().to_numpy(), color='navy')
g.ax_marg_x.bar(np.arange(0.5, D), df.groupby(['day'])['Cost Dif.'].sum().to_numpy(), color='navy')

g.ax_joint.set_xticks(np.arange(0.5, D))
g.ax_joint.set_xticklabels(range(1, D + 1), rotation=0)
g.ax_joint.set_yticks(np.arange(0.5, H))
g.ax_joint.set_yticklabels(range(H), rotation=0)

# remove ticks between heatmao and histograms
g.ax_marg_x.tick_params(axis='x', bottom=False, labelbottom=False)
g.ax_marg_y.tick_params(axis='y', left=False, labelleft=False)
# remove ticks showing the heights of the histograms
g.ax_marg_x.tick_params(axis='y', left=False, labelleft=False)
g.ax_marg_y.tick_params(axis='x', bottom=False, labelbottom=False)

g.fig.set_size_inches(20, 8)  # jointplot creates its own figure, the size can only be changed afterwards
# g.fig.subplots_adjust(hspace=0.3) # optionally more space for the tick labels
g.fig.subplots_adjust(hspace=0.05, wspace=0.02)  # less spaced needed when there are no tick labels
plt.show()