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:

pairplot (well, the underlying PairGrid) now delegates hue to the mapped function if it supports it. You'll probably not want to draw a distinct marginal density for each price, though:

diamonds = sns.load_dataset("diamonds")sns.pairplot(    diamonds,    hue="price", vars=["carat", "depth", "table"],     diag_kws=dict(color=".2", hue=None))

Old answer, useful for demonstrating more flexible use of PairGrid:

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))

In the documentation ( https://seaborn.pydata.org/generated/seaborn.pairplot.html ), you can see that you just need to set palette = my_palette when calling the function like so:

sns.pairplot(train_df, palette = my_palette)