Seaborn pairplots with continuous hues?

Question

How may I introduce a continuous hue to my seaborn pairplots?

I am passing in a pandas data frame train_df in order to visualise the relationship between the multiple features.

However I'd also like to add a hue which would use their corresponding target values, target_df. These target values are on a continuous scale (~ floats between 10 and 100).

I have defined a sns.color_palette("RdGr") that I'd like to use.

Right now I have following pairplot (with no hue):

sns.pairplot(train_df)

How can I pass in the target_df as a hue using color palette defined above?

Many thanks in advance.

Answer 1

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:

Answer 2

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))