如何在单个窗口中绘制多个seaborn`distplot`？

Question

我试图在一个窗口中绘制多个 seaborn distplot 。 我知道如何为单个数据列表生成密度图，如下面的代码所示（ make_density函数）。 但是，我不确定如何在单个窗口下绘制多个 seaborn distplots 。 假设我的列表stat_list包含6只列出作为其元素，在这里我要画一个distplot从每个下这6名名单stat_list 。 我怎么可以得出6个displots下，同一个窗口，其中3个地块显示每行（让我的输出将有2排3个区）的？

谢谢，

# function to plot the histogram for a single list.
def make_density(stat_list, color, x_label, y_label):
    
    # Plot formatting
    plt.xlabel(x_label)
    plt.ylabel(y_label)

    # Draw the histogram and fit a density plot.
    sns.distplot(stat_list, hist = True, kde = True,
                 kde_kws = {'linewidth': 2}, color=color)
    
    # get the y-coordinates of the points of the density curve.
    dens_list = sns.distplot(stat_list, hist = False, kde = False,
             kde_kws = {'linewidth': 2}, color = color).get_lines()[0].get_data()[1].tolist()
        
    # find the maximum y-coordinates of the density curve.            
    max_dens_index = dens_list.index(max(dens_list))
    
    # find the mode of the density plot.
    mode_x = sns.distplot(stat_list, hist = False, kde = False,
             kde_kws = {'linewidth': 2}, color = color).get_lines()[0].get_data()[0].tolist()[max_dens_index]
    
    # draw a vertical line at the mode of the histogram.
    plt.axvline(mode_x, color='blue', linestyle='dashed', linewidth=1.5)
    plt.text(mode_x * 1.05, 0.16, 'Mode: {:.4f}'.format(mode_x))

# `stat_list` is a list of 6 lists
# I want to draw histogram and density plot of 
# each of these 6 lists contained in `stat_list` in a single window,
# where each row containing the histograms and densities of the 3 plots
# so in my example, there would be 2 rows of 3 columns of plots (2 x 3 =6).
stat_list = [[0.3,0.5,0.7,0.3,0.5],[0.2,0.1,0.9,0.7,0.4],[0.9,0.8,0.7,0.6,0.5]
          [0.2,0.6,0.75,0.87,0.91],[0.2,0.3,0.8,0.9,0.3],[0.2,0.3,0.8,0.87,0.92]]

Answer 1

为此，我会使用 seaborn 的FacetGrid类。

简单版：

import seaborn
seaborn.set(style='ticks', context='paper')

axgrid = (
    seaborn.load_dataset('titanic')
        .pipe(seaborn.FacetGrid, hue='deck', col='deck', col_wrap=3, sharey=False)
        .map(seaborn.distplot, 'fare')
)

或者对您的功能进行一些修改：

from matplotlib import pyplot
import seaborn
seaborn.set(style='ticks', context='paper')


# function to plot the histogram for a single list.
def make_density(stat, color=None, x_label=None, y_label=None, ax=None, label=None):
   
    if not ax:
        ax = pyplot.gca()
    # Draw the histogram and fit a density plot.
    seaborn.distplot(stat, hist=True, kde=True,
                     kde_kws={'linewidth': 2}, color=color, ax=ax)

    # get the y-coordinates of the points of the density curve.
    dens_list = ax.get_lines()[0].get_data()[1]

    # find the maximum y-coordinates of the density curve.
    max_dens_index = dens_list.argmax()

    # find the mode of the density plot.
    mode_x = ax.get_lines()[0].get_data()[0][max_dens_index]

    # draw a vertical line at the mode of the histogram.
    ax.axvline(mode_x, color=color, linestyle='dashed', linewidth=1.5)
    ymax = ax.get_ylim()[1]
    ax.text(mode_x * 1.1, ymax * 0.16, 'Mode: {:.4f}'.format(mode_x))

    # Plot formatting
    ax.set_xlabel(x_label)
    ax.set_ylabel(y_label)


axgrid = (
    seaborn.load_dataset('titanic')
        .pipe(seaborn.FacetGrid, hue='deck', col='deck', col_wrap=3, sharey=False)
        .map(make_density, 'fare')
)

Answer 2

您可以使用fig, axes = plt.subplots(...)创建子fig, axes = plt.subplots(...)网格。 然后，您可以提供返回的“轴”的每个“轴”作为sns.distplot()的ax=参数。 请注意，您需要使用相同的ax来设置标签， plt.xlabel()只会更改其中一个子图。

不建议调用sns.distplot三次。 sns.distplot将为同一个ax添加越来越多的信息。 另请注意，您可以使用诸如argmax()类的 numpy 函数来有效地找到最大值，而无需转换为 Python 列表（当有大量数据时会很慢）。

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

# function to plot the histogram for a single list.
def make_density(stat, color, x_label, y_label, ax):
    # Draw the histogram and fit a density plot.
    sns.distplot(stat, hist=True, kde=True,
                 kde_kws={'linewidth': 2}, color=color, ax=ax)

    # get the y-coordinates of the points of the density curve.
    dens_list = ax.get_lines()[0].get_data()[1]

    # find the maximum y-coordinates of the density curve.
    max_dens_index = dens_list.argmax()

    # find the mode of the density plot.
    mode_x = ax.get_lines()[0].get_data()[0][max_dens_index]

    # draw a vertical line at the mode of the histogram.
    ax.axvline(mode_x, color='blue', linestyle='dashed', linewidth=1.5)
    ax.text(mode_x * 1.05, 0.16, 'Mode: {:.4f}'.format(mode_x))

    # Plot formatting
    ax.set_xlabel(x_label)
    ax.set_ylabel(y_label)

stat_list = [[0.3, 0.5, 0.7, 0.3, 0.5], [0.2, 0.1, 0.9, 0.7, 0.4], [0.9, 0.8, 0.7, 0.6, 0.5],
             [0.2, 0.6, 0.75, 0.87, 0.91], [0.2, 0.3, 0.8, 0.9, 0.3], [0.2, 0.3, 0.8, 0.87, 0.92]]
num_subplots = len(stat_list)
ncols = 3
nrows = (num_subplots + ncols - 1) // ncols
fig, axes = plt.subplots(ncols=ncols, nrows=nrows, figsize=(ncols * 6, nrows * 5))
colors = plt.cm.tab10.colors
for ax, stat, color in zip(np.ravel(axes), stat_list, colors):
    make_density(stat, color, 'x_label', 'y_label', ax)
for ax in np.ravel(axes)[num_subplots:]:  # remove possible empty subplots at the end
    ax.remove()
plt.show()

PS：也可以使用histplot （Seaborn 0.11新内容）代替distplot 。 这应该会给出一个更好的图，尤其是当数据很少和/或离散时。

sns.histplot(stat, kde=True, line_kws={'linewidth': 2}, color=color, ax=ax)