Set same color palette for multiple plots from several dataframes

Question

I am using seaborn and and t-SNE to visualise class separability/overlap and in my dataset containing five classes. My plot is thus a 2×2 subplots. I used the following function which generates the figure below.

def pair_plot_tsne(df):

    tsne = TSNE(verbose=1, random_state=234) 

    df1 = df[(df['mode'] != 'car') & (df['mode'] != 'bus')]
    tsne1 = tsne.fit_transform(df1[cols].values) # cols - df's columns list
    df1['tsne_one'] = tsne1[:, 0]
    df1['tsne-two'] = tsne1[:, 1]

    df2 = df[(df['mode'] != 'foot') & (df['mode']!= 'bus')]
    tsne2 = tsne.fit_transform(df2[cols].values)
    df2['tsne_one'] = tsne2[:, 0]
    df2['tsne-two'] = tsne2[:, 1]

    df3 = df[df['mode'] != 'car']
    tsne3 = tsne.fit_transform(df3[cols].values)
    df3['tsne_one'] = tsne3[:, 0]
    df3['tsne-two'] = tsne3[:, 1]

    df4 = df[df['mode'] != 'foot']
    tsne4 = tsne.fit_transform(df4[cols].values)
    df4['tsne_one'] = tsne4[:, 0]
    df4['tsne-two'] = tsne4[:, 1]

    #create figure
    f = plt.figure(figsize=(16,4))

    ax1 = plt.subplot(2, 2, 1)
    sns.scatterplot( #df1 has 3 classes, so 3 colors
        x ='tsne_one', y='tsne-two', hue = 'mode', data = df1, palette = sns.color_palette('hls', 3), 
        legend='full', alpha = 0.7, ax = ax1 )

    ax2 = plt.subplot(2, 2, 2)
    sns.scatterplot( #df2 has 3 classes, so 3 colors
        x ='tsne_one', y='tsne-two', hue = 'mode', data = df2, palette = sns.color_palette('hls', 3), 
        legend='full', alpha = 0.7, ax = ax2 )

    ax3 = plt.subplot(2, 2, 3)
    sns.scatterplot( #df3 has 4 classes, so 4 colors
        x ='tsne_one', y='tsne-two', hue = 'mode', data = df3, palette = sns.color_palette('hls', 4), 
        legend='full', alpha = 0.7, ax = ax3 )

    ax4 = plt.subplot(2, 2, 4)
    sns.scatterplot( #df4 has 4 classes, so 4 colors
        x ='tsne_one', y='tsne-two', hue = 'mode', data = df4, palette = sns.color_palette('hls', 4),
        legend='full', alpha = 0.7, ax = ax4 )

    return f, ax1, ax2, ax3, ax4

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.

Asked By: arilwan

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Source

Answer 1

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()

Answered By: JohanC

Set same color palette for multiple plots from several dataframes

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