为多个数据帧的多个绘图设置相同的调色板

Question

我正在使用 seaborn 和 t -SNE 可视化类可分离性/重叠，并在我的数据集中包含五个类。因此，我的情节是 2x2 子情节。我使用以下函数生成下图。

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

我希望每个类的颜色保持一致，无论它出现在哪个图中。对于类，无论出现在哪个子图中，汽车模式的蓝色颜色，总线模式的黑色颜色它出现在哪个绘图中，等等...

现在，foot 在 subplot(2, 2, 1) 中是红色的，car< /code> 在 subplot(2, 2, 2)虽然其余部分是一致的。

Answer 1

对于这个用例，seaborn 允许字典作为调色板。字典将为每个色调值分配一个颜色。

以下是如何为您的数据创建此类字典的示例：

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

# create reproducible random data
np.random.seed(2023)
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)})

# create an array with all unique categories from the 'mode' column in all dataframes 
modes = pd.concat([df['mode'] for df in (df1, df2, df3, df4)], ignore_index=True).unique()

# create a color palette for the number of values in modes
colors = sns.color_palette('hls', len(modes))

# create a dictionary of modes and colors
palette = dict(zip(modes, colors))

# create the figure and subplot axes
fig, axs = plt.subplots(2, 2, figsize=(12,6), tight_layout=True)

# zip each dataframe to an axes and iterate through each pair
for df, ax in zip((df1, df2, df3, df4), axs.flatten()):

    # plot each data from to a different axes, and color with the custom palette to have the same colors 
    sns.scatterplot(x='tsne_one', y='tsne-two', hue='mode', data=df, palette=palette, legend='full', alpha=0.7, ax=ax)
plt.show()

---

您可能还需要单个人物级别图例

from matplotlib.lines import Line2D

fig, axs = plt.subplots(2, 2, figsize=(12,6), tight_layout=True)

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)
    # remove the subplot legend
    ax.legend().remove()

# create the custom legend handles
handles = [Line2D([0], [0], marker='o', markerfacecolor=c, linestyle='', markeredgecolor='none') for c in colors]

# create a single figure legend
fig.legend(handles, modes, title='Mode', bbox_to_anchor=(1, 0.5), loc='center left', frameon=False)