In fact it is not a trivial matter.
pairplot
has not ax
parameter to make it simple due to its nature of "figure-level" at seaborn.
There are some hacks:
How to plot multiple Seaborn Jointplot in Subplot
A "clean" one consists in saving the plots as images to bring them back to place them: https://stackoverflow.com/a/61330067/10372616
Using it in your plots:
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import seaborn as sns
data = sns.load_dataset("iris")
def pairplot(col,hue_var):
sns.set(style="ticks")
cols_to_look_at = col + [hue_var]
return sns.pairplot(data[cols_to_look_at], hue=hue_var) # !!! I've placed a return
col_names1 = ['sepal_length', 'sepal_width']
col_names2 = ['petal_length', 'petal_width']
g0 = pairplot(col_names1,'species')
g1 = pairplot(col_names2,'species')
############### 1. SAVE PLOTS IN MEMORY TEMPORALLY
g0.savefig('g0.png', dpi=300)
plt.close(g0.fig)
g1.savefig('g1.png', dpi=300)
plt.close(g1.fig)
############### 2. CREATE YOUR SUBPLOTS FROM TEMPORAL IMAGES
f, axarr = plt.subplots(1, 2, figsize=(20, 20))
axarr[0].imshow(mpimg.imread('g0.png'))
axarr[1].imshow(mpimg.imread('g1.png'))
# turn off x and y axis
[ax.set_axis_off() for ax in axarr.ravel()]
plt.tight_layout()
plt.show()