如何在组合柱状图与热力图时对齐Y轴刻度标签

问题描述 #

使用Seaborn组合两个横向柱状图与一个热力图时，开启sharey="row"后出现以下问题：

• Y轴标签与右侧热力图对齐
• 左侧柱状图顶部被截断，且柱状图与Y轴标签无法对齐

原始可运行代码如下：

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

### Generate example data
np.random.seed(123)
year = [2018, 2019, 2020, 2021]
task = [x + 2 for x in range(18)]
student = [x for x in range(200)]
amount = [x + 10 for x in range(90)]
violation = [letter for letter in "thisisjustsampletextforlabels"] # one letter labels

df_example = pd.DataFrame({
    # some ways to create random data
    'year':np.random.choice(year,500),
    'task':np.random.choice(task,500),
    'violation':np.random.choice(violation, 500),
    'amount':np.random.choice(amount, 500),
    'student':np.random.choice(student, 500)
})

### My code
temp = df_example.groupby(["violation"])["amount"].sum().sort_values(ascending = False).reset_index()
total_violations = temp["amount"].sum()
sns.set(font_scale = 1.2)

f, axs = plt.subplots(1,3,
                      figsize=(5,5),
                      sharey="row",
                      gridspec_kw=dict(width_ratios=[3,1.5,5]))

# Plot frequency
df1 = df_example.groupby(["year","violation"])["amount"].sum().sort_values(ascending = False).reset_index()
frequency = sns.barplot(data = df1, y = "violation", x = "amount", log = True, ax=axs[0])

# Plot percent
df2 = df_example.groupby(["violation"])["amount"].sum().sort_values(ascending = False).reset_index()
total_violations = df2["amount"].sum()
percent = sns.barplot(x='amount', y='violation', estimator=lambda x: sum(x) / total_violations * 100, data=df2, ax=axs[1])

# Pivot table and plot heatmap 
df_heatmap = df_example.groupby(["violation", "task"])["amount"].sum().sort_values(ascending = False).reset_index()
df_heatmap_pivot = df_heatmap.pivot("violation", "task", "amount")
df_heatmap_pivot = df_heatmap_pivot.reindex(index=df_heatmap["violation"].unique())
heatmap = sns.heatmap(df_heatmap_pivot, fmt = "d", cmap="Greys", norm=LogNorm(), ax=axs[2])
plt.subplots_adjust(top=1)

axs[2].set_facecolor('xkcd:white')
axs[2].set(ylabel="",xlabel="Task")

axs[0].set_xlabel('Total amount of violations per year')
axs[1].set_xlabel('Percent (%)')

axs[1].set_ylabel('')
axs[0].set_ylabel('Violation')

解决方案 #

核心问题是Seaborn热力图会自动调整轴的边距和范围，破坏了sharey的对齐效果。通过以下修改解决：

修改后的完整代码：

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

### Generate example data
np.random.seed(123)
year = [2018, 2019, 2020, 2021]
task = [x + 2 for x in range(18)]
student = [x for x in range(200)]
amount = [x + 10 for x in range(90)]
violation = [letter for letter in "thisisjustsampletextforlabels"] # one letter labels

df_example = pd.DataFrame({
    # some ways to create random data
    'year':np.random.choice(year,500),
    'task':np.random.choice(task,500),
    'violation':np.random.choice(violation, 500),
    'amount':np.random.choice(amount, 500),
    'student':np.random.choice(student, 500)
})

### Modified code
temp = df_example.groupby(["violation"])["amount"].sum().sort_values(ascending = False).reset_index()
total_violations = temp["amount"].sum()
sns.set(font_scale = 1.2)

# 增大figsize避免截断，优化宽度比例
f, axs = plt.subplots(1,3,
                      figsize=(12,8),
                      sharey="row",
                      gridspec_kw=dict(width_ratios=[3,1.5,5]))

# Plot frequency
df1 = df_example.groupby(["year","violation"])["amount"].sum().sort_values(ascending = False).reset_index()
frequency = sns.barplot(data = df1, y = "violation", x = "amount", log = True, ax=axs[0])

# Plot percent
df2 = df_example.groupby(["violation"])["amount"].sum().sort_values(ascending = False).reset_index()
total_violations = df2["amount"].sum()
percent = sns.barplot(x='amount', y='violation', estimator=lambda x: sum(x) / total_violations * 100, data=df2, ax=axs[1])

# Pivot table and plot heatmap 
df_heatmap = df_example.groupby(["violation", "task"])["amount"].sum().sort_values(ascending = False).reset_index()
df_heatmap_pivot = df_heatmap.pivot("violation", "task", "amount")
df_heatmap_pivot = df_heatmap_pivot.reindex(index=df_heatmap["violation"].unique())

# 绘制热力图时禁用颜色条（可选，如需颜色条需手动调整位置），并设置yticklabels匹配
heatmap = sns.heatmap(df_heatmap_pivot, fmt = "d", cmap="Greys", norm=LogNorm(), 
                      ax=axs[2], cbar=False, yticklabels=True)

# 同步所有子图的Y轴范围，确保对齐
y_min, y_max = axs[0].get_ylim()
axs[1].set_ylim(y_min, y_max)
axs[2].set_ylim(y_min, y_max)

# 调整子图间距，避免重叠
plt.subplots_adjust(top=0.95, bottom=0.05, wspace=0.2)

axs[2].set_facecolor('xkcd:white')
axs[2].set(ylabel="",xlabel="Task")

axs[0].set_xlabel('Total amount of violations per year')
axs[1].set_xlabel('Percent (%)')

axs[1].set_ylabel('')
axs[0].set_ylabel('Violation')

plt.show()

关键修改说明 #

• 增大figsize为(12,8)，避免顶部内容被截断；
• 热力图添加cbar=False（若需要颜色条，可使用plt.colorbar(heatmap.collections[0], ax=axs[2])手动放置）；
• 获取第一个柱状图的Y轴范围，强制所有子图使用相同范围，确保对齐；
• 调整plt.subplots_adjust()的参数，优化子图间距。

内容的提问来源于stack exchange，提问作者Linus Östlund