这将工作：

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

s = 'Session,Subject,Age,Pre_Post\nPre,SY01,young,14.0\nPre,SY02,young,14.0\nPre,SY03,young,13.0\nPre,SY04,young,13.0\nPre,SY05,young,13.0\nPre,SY06,young,15.0\nPre,SY07,young,14.0\nPre,SY08,young,14.0\nPre,SA01,old,5.0\nPre,SA02,old,1.0\nPre,SA03,old,10.0\nPre,SA04,old,3.0\nPre,SA05,old,9.0\nPre,SA06,old,5.0\nPre,SA07,old,13.0\nPre,SA08,old,13.0\nPost,SY01,young,14.0\nPost,SY02,young,13.0\nPost,SY03,young,14.0\nPost,SY04,young,13.0\nPost,SY05,young,15.0\nPost,SY06,young,13.0\nPost,SY07,young,15.0\nPost,SY08,young,14.0\nPost,SA01,old,6.0\nPost,SA02,old,2.0\nPost,SA03,old,10.0\nPost,SA04,old,7.0\nPost,SA05,old,8.0\nPost,SA06,old,11.0\nPost,SA07,old,14.0\nPost,SA08,old,11.0'

a = np.array([r.split(',') for r in s.split('\n')])

test_data = pd.DataFrame(a[1:, :], columns = a[0])
test_data['Pre_Post'] = test_data['Pre_Post'].apply(float)
def encode_session(x):
  if x=='Pre':
    return 0
  else:
    return 1
test_data['Session'] = test_data['Session'].apply(encode_session)

test_data2 = test_data.copy()
def offset_session(row):
  if row['Age']=='young':
    return row['Session']-0.2
  else:
    return row['Session']+0.2
test_data2['Session'] = test_data2.apply(offset_session, axis=1)

fig, ax = plt.subplots(figsize=(7,5))
sns.boxplot(data=test_data, 
            x="Session", 
            y="Pre_Post", 
            hue="Age", 
            palette="pastel", 
            #boxprops=boxprops, 
            ax=ax)

sns.swarmplot(data=test_data, 
              x="Session", 
              y="Pre_Post", 
              hue="Age", 
              dodge=True, 
              palette="dark", 
              ax=ax)
    
sns.lineplot(data=test_data2, 
                 x="Session", 
                 y="Pre_Post", 
                 hue="Age", 
                 estimator=None, 
                 units="Subject", 
                 style="Age", 
                 markers=True, 
                 palette="dark", 
                 ax=ax)

plt.title("Test")
plt.xlabel("Session")
plt.ylabel("Score")

# Move the legend outside the plot
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)

plt.xticks([0,1],['Pre', 'Post'])

plt.show()

我们可以讨论这个图的优点。它肯定是混乱的，可能会更好地在两个单独的轴上分裂，减少数据相互重叠。我个人不认为条形图更好。之前/之后的线图可以很好地讲述故事。例如，在one below that I found on google中，我更喜欢在条形图中看这个~40对条形图：

• 可视化的目的是使从数据中提取意义变得更容易。
• 通常将swarmplot放在boxplot上，因为它提供了有关发行版的其他信息。
• 可以，但不应该在分布图上放置趋势线。这是两种类型的图，它们传达有关数据的不同信息，并且图变得难以解释。
• 由于重点是显示数据的分布，和到清楚地显示了每个'Subject'的'Score'变化，一个barplot更合适。
• 这也是一个更清晰的可视化来分离'Age'组。
• 如另一个答案所示：
• a trendline can be shown between associated markers。但是，结果很难读;几乎不可能辨别出相关的标记
• 请求是为每个'Age'的每个标记添加从'Pre'到'Post'的趋势线，这会导致plot难以读取**，即使是数据的一个小子集**。
• 当有许多标记时，趋势线将只指向中心标记，因为lineplot无法与从swarmplot开始的标记对齐。

导入和数据

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

# data string
s = 'Session,Subject,Age,Pre_Post\nPre,SY01,young,14.0\nPre,SY02,young,14.0\nPre,SY03,young,13.0\nPre,SY04,young,13.0\nPre,SY05,young,13.0\nPre,SY06,young,15.0\nPre,SY07,young,14.0\nPre,SY08,young,14.0\nPre,SA01,old,5.0\nPre,SA02,old,1.0\nPre,SA03,old,10.0\nPre,SA04,old,3.0\nPre,SA05,old,9.0\nPre,SA06,old,5.0\nPre,SA07,old,13.0\nPre,SA08,old,13.0\nPost,SY01,young,14.0\nPost,SY02,young,13.0\nPost,SY03,young,14.0\nPost,SY04,young,13.0\nPost,SY05,young,15.0\nPost,SY06,young,13.0\nPost,SY07,young,15.0\nPost,SY08,young,14.0\nPost,SA01,old,6.0\nPost,SA02,old,2.0\nPost,SA03,old,10.0\nPost,SA04,old,7.0\nPost,SA05,old,8.0\nPost,SA06,old,11.0\nPost,SA07,old,14.0\nPost,SA08,old,11.0'

# split the data into separate components
data = [v.split(',') for v in s.split('\n')]

# load the list of lists into a dataframe
df = pd.DataFrame(data=data[1:], columns=data[0])

# rename the column
df.rename({'Pre_Post': 'Score'}, axis=1, inplace=True)

# convert the column from a string to a float
df['Score'] = df['Score'].apply(float)

# create separate groups of data for the ages
(_, old), (_, young) = df.groupby('Age')

old

Session Subject  Age  Score
8      Pre    SA01  old    5.0
9      Pre    SA02  old    1.0
10     Pre    SA03  old   10.0
11     Pre    SA04  old    3.0
12     Pre    SA05  old    9.0
13     Pre    SA06  old    5.0
14     Pre    SA07  old   13.0
15     Pre    SA08  old   13.0
24    Post    SA01  old    6.0
25    Post    SA02  old    2.0
26    Post    SA03  old   10.0
27    Post    SA04  old    7.0
28    Post    SA05  old    8.0
29    Post    SA06  old   11.0
30    Post    SA07  old   14.0
31    Post    SA08  old   11.0

young

Session Subject    Age  Score
0      Pre    SY01  young   14.0
1      Pre    SY02  young   14.0
2      Pre    SY03  young   13.0
3      Pre    SY04  young   13.0
4      Pre    SY05  young   13.0
5      Pre    SY06  young   15.0
6      Pre    SY07  young   14.0
7      Pre    SY08  young   14.0
16    Post    SY01  young   14.0
17    Post    SY02  young   13.0
18    Post    SY03  young   14.0
19    Post    SY04  young   13.0
20    Post    SY05  young   15.0
21    Post    SY06  young   13.0
22    Post    SY07  young   15.0
23    Post    SY08  young   14.0

绘图

• 真实的数据可能有更多的观测值，因此增加figsize元组中的第二个数字以增加图的长度，并调整height_ratios中的第二个数字以使条形图使用更多的图形。

# create the figure using height_ratios to make the bottom subplots larger than the top subplots
fig, axes = plt.subplots(2, 2, figsize=(11, 11), height_ratios=[1, 2])

# flatten the axes for easy access
axes = axes.flat

# plot the boxplots
sns.boxplot(data=young, x="Session", y="Score", ax=axes[0])
sns.boxplot(data=old, x="Session", y="Score", ax=axes[1])

# plot the swarmplots
sns.swarmplot(data=young, x="Session", y="Score", hue='Session', edgecolor='k', linewidth=1, legend=None, ax=axes[0])
sns.swarmplot(data=old, x="Session", y="Score", hue='Session', edgecolor='k', linewidth=1, legend=None, ax=axes[1])

# add a title
axes[0].set_title('Age: Young', fontsize=15)
axes[1].set_title('Age: Old', fontsize=15)

# add the barplots
sns.barplot(data=young, x='Score', y='Subject', hue='Session', ax=axes[2])
sns.barplot(data=old, x='Score', y='Subject', hue='Session', ax=axes[3])

# extract the axes level legend properties
handles, labels = axes[3].get_legend_handles_labels()

# iterate through the bottom axes
for ax in axes[2:]:
    # removed the axes legend
    ax.legend().remove()
    
    # iterate through the containers
    for c in ax.containers:
        
        # annotate the bars
        ax.bar_label(c, label_type='center')
    
# add a figure level legend
_ = fig.legend(handles, labels, title='Session', loc='outside right center', frameon=False)

可视化易读