关于多个岭图，我如何 plot 多个子图？ （海运）

Question

Recently, I want to plot several multiple ridge plots in a single plot. Thus, I try to use the subplot command to achieve this goal. However, it seems that it doesn't work. When I run the following code fragment, it should output a single picture containing two ridge plots. However, in fact, the following code fragment will output three figures, including two blank figures. How can I achieve my goal?

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

for i in range(2):
    plt.subplot(1, 2, i + 1)
    sns.set_theme(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})

    # Create the data
    rs = np.random.RandomState(1979)
    x = rs.randn(500)
    g = np.tile(list("ABCDEFGHIJ"), 50)
    df = pd.DataFrame(dict(x=x, g=g))
    m = df.g.map(ord)
    df["x"] += m

    # Initialize the FacetGrid object
    pal = sns.cubehelix_palette(10, rot=-.25, light=.7)
    g = sns.FacetGrid(df, row="g", hue="g", aspect=15, height=.5, palette=pal)

    # Draw the densities in a few steps
    g.map(sns.kdeplot, "x",
          bw_adjust=.5, clip_on=False,
          fill=True, alpha=1, linewidth=1.5)
    g.map(sns.kdeplot, "x", clip_on=False, color="w", lw=2, bw_adjust=.5)
    g.map(plt.axhline, y=0, lw=2, clip_on=False)


    # Define and use a simple function to label the plot in axes coordinates
    def label(x, color, label):
        ax = plt.gca()
        ax.text(0, .2, label, fontweight="bold", color=color,
                ha="left", va="center", transform=ax.transAxes)


    g.map(label, "x")

    # Set the subplots to overlap
    g.fig.subplots_adjust(hspace=-.25)

    # Remove axes details that don't play well with overlap
    g.set_titles("")
    g.set(yticks=[])
    g.despine(bottom=True, left=True)

plt.show()

Answer 1

The code below creates two columns of ridge plots for two dataframes. It supposes the dataframes have a similar structure. For seaborn to work well, the dataframes are combined together. Note that a lot of tweaking may be needed to get a good-looking result, depending on your specific data.

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

sns.set_theme(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})
rs = np.random.RandomState(1979)
# create a first data frame
x1 = rs.randn(500)
g1 = np.repeat(list("ABCDEFGHIJ"), 50)
df1 = pd.DataFrame(dict(x=x1 + np.repeat(np.arange(2, 22, 2), 50), g=g1))

# create a second data frame of a similar form
x2 = rs.randn(700)
g2 = np.repeat(list("ABCDEFGHIJ"), 70)
df2 = pd.DataFrame(dict(x=x2 * 0.5 + np.repeat(np.arange(2, 22), 35), g=g2))

df1['source'] = 'df1'
df2['source'] = 'df2'
df = pd.concat([df1, df2])
df['hue'] = df['source'] + df['g']

# Initialize the FacetGrid object
pal1 = sns.cubehelix_palette(10, rot=-.15, light=.7)
pal2 = sns.cubehelix_palette(10, start=.4, rot=.15, light=.7)

g = sns.FacetGrid(df, row="g", hue="hue", col="source", aspect=15, height=.5, palette=pal1 + pal2)

# Draw the densities in a few steps
g.map(sns.kdeplot, "x",
      bw_adjust=.5, clip_on=False,
      fill=True, alpha=1, linewidth=1.5)
g.map(sns.kdeplot, "x", clip_on=False, color="w", lw=2, bw_adjust=.5)
g.map(plt.axhline, y=0, lw=2, clip_on=False)

# Define and use a simple function to label the plot in axes coordinates
def label(x, color, label):
    ax = plt.gca()
    ax.text(0, .2, label[-1:], fontweight="bold", color=color,
            ha="left", va="center", transform=ax.transAxes)

g.map(label, "x")

# Set the subplots to overlap
g.fig.subplots_adjust(hspace=-.25)

# Remove axes details that don't play well with overlap
g.set_titles("")
g.set(yticks=[])
g.set(xlabel="")
g.despine(bottom=True, left=True)

plt.show()