Seaborn set color for unique categorical over several pair-plots-CodePudding

I am using seaborn and and

Since I'm plotting a subset of the dataset in each subplot, I would like to have the color of each class consistent, in whichever plot it appears. For class, a blue color for the car mode in whichever subplot it appears, a black color for bus mode in which ever plot it appears, etc...

As it is now, foot is red in subplot(2, 2, 1), and also car is read in subplot(2, 2, 2) although the rest are consistent.

CodePudding user response：

For this use case, seaborn allows a dictionary as palette. The dictionary will assign a color to each hue value.

Here is an example of how such a dictionary could be created for your data:

from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

df1 = pd.DataFrame({'tsne_one': np.random.randn(10),
                    'tsne-two': np.random.randn(10),
                    'mode': np.random.choice(['foot', 'metro', 'bike'], 10)})
df2 = pd.DataFrame({'tsne_one': np.random.randn(10),
                    'tsne-two': np.random.randn(10),
                    'mode': np.random.choice(['car', 'metro', 'bike'], 10)})
df3 = pd.DataFrame({'tsne_one': np.random.randn(10),
                    'tsne-two': np.random.randn(10),
                    'mode': np.random.choice(['foot', 'bus', 'metro', 'bike'], 10)})
df4 = pd.DataFrame({'tsne_one': np.random.randn(10),
                    'tsne-two': np.random.randn(10),
                    'mode': np.random.choice(['car', 'bus', 'metro', 'bike'], 10)})
modes = pd.concat([df['mode'] for df in (df1, df2, df3, df4)], ignore_index=True).unique()
colors = sns.color_palette('hls', len(modes))
palette = {mode: color for mode, color in zip(modes, colors)}

fig, axs = plt.subplots(2, 2, figsize=(12,6))
for df, ax in zip((df1, df2, df3, df4), axs.flatten()):
    sns.scatterplot(x='tsne_one', y='tsne-two', hue='mode', data=df, palette=palette, legend='full', alpha=0.7, ax=ax)

plt.tight_layout()
plt.show()