Combining two heat maps in seaborn

Question

I have 2 data tables with the dimensions 4x25. Each table is from a different point in time, but has exactly the same meta data, in essence the same column and row

Answer 1

The best part about matplotlib/seaborn libraries is that everything is plotted in the same figure until you clear it. You can use the mask argument in sns.heatmap to get a diagonal heatmap plot. To get a "mixed" heatmap, such that you can have two different types of data plotted with different colormaps, you can do something like this:

from sklearn.datasets import load_iris
import seaborn as sns
import pandas as pd
import numpy as np

data = load_iris()
df= pd.DataFrame(data.data,columns = data.feature_names)
df['target'] = data.target

df_0 = df[df['target']==0]
df_1 = df[df['target']==1]

df_0.drop('target',axis=1,inplace=True)
df_1.drop('target',axis=1,inplace=True)

matrix_0 = np.triu(df_0.corr())
matrix_1 = np.tril(df_1.corr())

import seaborn as sns
from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable
from mpl_toolkits.axes_grid1.colorbar import colorbar
sns.heatmap(df_0.corr(),annot=True,mask=matrix_0,cmap="BuPu")
sns.heatmap(df_1.corr(),annot=True,mask=matrix_1,cmap="YlGnBu")

Hope this is what your second idea was. Note that this will only work when you have same column names.