import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import ptitprince as pt雨云图（Python）
雨云图将半小提琴（「云」）、箱线图与抖动散点（「雨」）组合在一张图中，同时呈现完整分布、汇总统计量与单个观测值。在生物医学研究中，它已成为分组连续型数据条形图的首选替代方案。Python 中 ptitprince 包在 seaborn 之上组装这三个图层。
示例
环境配置
- 系统要求：跨平台（Linux/MacOS/Windows）
- 编程语言：Python
- 依赖包：
matplotlib、seaborn、pandas、numpy、ptitprince
数据准备
我们模拟三种刺激条件下流式检测的响应强度，每个条件足够的细胞数使半小提琴轮廓具有信息量。
np.random.seed(41)
conditions = ['Unstim', 'Anti-CD3', 'Anti-CD3/28']
n_cells = 250
df = pd.DataFrame({
'Condition': np.repeat(conditions, n_cells),
'Response': np.concatenate([
np.random.gamma(4.0, 0.5, n_cells),
np.random.gamma(5.0, 0.55, n_cells),
np.random.gamma(7.0, 0.6, n_cells),
])
})
palette = {'Unstim': '#8da0cb', 'Anti-CD3': '#66c2a5', 'Anti-CD3/28': '#e63946'}可视化
基础雨云图
三个图层构成云朵：半小提琴位于类别刻度左侧，窄箱线图位于刻度上，抖动散点位于右侧。
dx = 'Condition'; dy = 'Response'; ort = 'v'
fig, ax = plt.subplots(figsize=(7, 4.6))
ax = pt.half_violinplot(x=dx, y=dy, hue=dx, data=df, palette=palette, order=conditions,
dodge=False, bw=0.2, cut=0, scale='area', width=0.6,
inner=None, orient=ort, ax=ax)
ax = sns.boxplot(x=dx, y=dy, data=df, order=conditions, orient=ort,
width=0.15, color='0.85', showcaps=True,
boxprops=dict(edgecolor='0.2'),
whiskerprops=dict(color='0.2'), capprops=dict(color='0.2'),
medianprops=dict(color='#e63946'),
showfliers=False, ax=ax)
ax = sns.stripplot(x=dx, y=dy, data=df, hue=dx, order=conditions, orient=ort,
palette=palette, legend=False, edgecolor='white', linewidth=0.3,
size=3, jitter=0.4, alpha=0.8, ax=ax)
if ax.get_legend() is not None:
ax.get_legend().remove()
ax.set_xlabel('Stimulation condition')
ax.set_ylabel('Response intensity (a.u.)')
ax.spines[['top', 'right']].set_visible(False)
plt.tight_layout()
plt.show()
横向雨云图
类别标签较长时，横向布局（orient='h'）更易读，云朵悬挂在箱体上方。注意 ptitprince 在横向模式下要求 x 为数值变量、y 为类别变量。
ort = 'h'
fig, ax = plt.subplots(figsize=(7, 4.6))
# 横向模式下 ptitprince 要求 x 为数值变量
ax = pt.half_violinplot(x=dy, y=dx, hue=dx, data=df, palette=palette, order=conditions,
dodge=False, bw=0.2, cut=0, scale='area', width=0.6,
inner=None, orient=ort, ax=ax)
ax = sns.boxplot(x=dy, y=dx, data=df, order=conditions, orient=ort,
width=0.15, color='0.85',
boxprops=dict(edgecolor='0.2'),
whiskerprops=dict(color='0.2'), capprops=dict(color='0.2'),
medianprops=dict(color='#e63946'),
showfliers=False, ax=ax)
ax = sns.stripplot(x=dy, y=dx, data=df, hue=dx, order=conditions, orient=ort,
palette=palette, legend=False, edgecolor='white', linewidth=0.3,
size=3, jitter=0.4, alpha=0.8, ax=ax)
if ax.get_legend() is not None:
ax.get_legend().remove()
ax.set_xlabel('Response intensity (a.u.)')
ax.set_ylabel('Stimulation condition')
ax.spines[['top', 'right']].set_visible(False)
plt.tight_layout()
plt.show()
分组雨云图
将第二个因素映射到 hue，把每个云朵拆分为成对的两半，例如每种刺激内的应答与非应答细胞。
ort = 'v'
df2 = df.copy()
df2['Subpopulation'] = np.random.choice(['Responder', 'Non-responder'], len(df2), p=[0.6, 0.4])
fig, ax = plt.subplots(figsize=(7.5, 4.6))
ax = pt.half_violinplot(x=dx, y=dy, hue='Subpopulation', data=df2, order=conditions,
hue_order=['Responder', 'Non-responder'],
palette=['#457b9d', '#e78ac3'],
bw=0.2, cut=0, scale='area', width=0.9,
inner=None, orient=ort, dodge=True, ax=ax)
ax = sns.boxplot(x=dx, y=dy, hue='Subpopulation', data=df2, order=conditions,
hue_order=['Responder', 'Non-responder'], orient=ort,
width=0.3, color='0.88',
boxprops=dict(edgecolor='0.2'),
whiskerprops=dict(color='0.2'), capprops=dict(color='0.2'),
medianprops=dict(color='#e63946'),
showfliers=False, dodge=True, ax=ax)
ax = sns.stripplot(x=dx, y=dy, hue='Subpopulation', data=df2, order=conditions,
hue_order=['Responder', 'Non-responder'],
palette=['#457b9d', '#e78ac3'], dodge=True,
edgecolor='white', linewidth=0.3, size=2.5, jitter=0.4,
alpha=0.8, ax=ax)
ax.set_xlabel('Stimulation condition')
ax.set_ylabel('Response intensity (a.u.)')
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles[:2], labels[:2], frameon=False, title=None, loc='upper left')
ax.spines[['top', 'right']].set_visible(False)
plt.tight_layout()
plt.show()/mnt/TMP/ipykernel_25641/1734680523.py:11: FutureWarning:
Setting a gradient palette using color= is deprecated and will be removed in v0.14.0. Set `palette='dark:0.88'` for the same effect.
ax = sns.boxplot(x=dx, y=dy, hue='Subpopulation', data=df2, order=conditions,
参考文献
- Allen, M., Poggiali, D., Whitaker, K., et al. (2019). Raincloud plots: a multi-platform tool for robust data visualization. Wellcome Open Research, 4, 63.
- Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90-95.
- Waskom, M. L. (2021). seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 3021.