Seaborn pairplots with continuous hues?
IIUC, you can just assign the target_df
as a column in train_df
and pass it as hue
:
sns.pairplot(data=train_df.assign(target=target_df,
hue='target')
However, this will be extremely slow if your target
is continuous. Instead, you can do a double for
loop:
num_features = len(train_df.columns)
fig,ax = plt.subplots(num_features, num_features, figsize=(10,10))
for i in train_df.columns:
for j in train_df.columns:
if i==j: # diagonal
sns.distplot(train_df[0], kde=False, ax=ax[i][j])
else: # off diagonal
sns.scatterplot(x=train_df[i],y=train_df[j],
ax=ax[i][j], hue=target_df, palette='BrBG',
legend=False)
Which gives you something like this:
This could be easier than it currently is, but it's not necessary to recreate PairGrid
yourself.
diamonds = sns.load_dataset("diamonds")
g = sns.PairGrid(diamonds, vars=["carat", "depth", "table"])
g.map_diag(sns.kdeplot, color=".2")
g.map_offdiag(sns.scatterplot, hue=diamonds["price"], s=5, linewidth=0)
g.axes[1, -1].legend(loc="center left", bbox_to_anchor=(.7, .5))