如何在Seaborn中仅绘制正面的误差线？

Question

我正在尝试绘制图表，但我有一些问题需要解决，对不起，但是我是程序语言的新手。第一：如何只绘制一张图表？我从互联网上得到了这个例子，当一个图表示每个代码有两个数字时，其中两个是空白。

[第二个：是否可以仅绘制正误差线？第三个：是否可以在一张图中并排绘制这两个图表？

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


Treat1 =pd.DataFrame({'Treatment': 1, 'weight': np.random.randint(low=1, high=100, size=40)})              
Treat2 =pd.DataFrame({'Treatment': 2, 'weight': np.random.randint(low=1, high=100, size=40)}) 

df = pd.concat([Treat1, Treat2])
Treat3 =pd.DataFrame({'Treatment': 1, 'weight': np.random.randint(low=100, high=300, size=40)})              
Treat4 =pd.DataFrame({'Treatment': 2, 'weight': np.random.randint(low=100, high=300, size=40)}) 

df2 = pd.concat([Treat3, Treat4])

sns.set(style="ticks")
fig, ax = plt.subplots()

color_map = dict(pos="indianred", neg="steelblue")
g = sns.catplot(x= "Treatment", y="weight",  hue="Treatment", capsize=.07, ci ="sd", 
                data=df,  kind="bar", palette = 'coolwarm', edgecolor="white")
plt.text(-0.22,99, "B")
plt.text(1.18,99, "A")
plt.ylabel('weight, kg')
plt.xticks([-0.2, 1.2], ['Group 1', 'Group 2'])
plt.ylim(0, 100)

color_map = dict(pos="indianred", neg="steelblue")
g = sns.catplot(x= "Treatment", y="weight",  hue="Treatment", capsize=.07, ci ="sd", 
                data=df2,  kind="bar", palette = 'coolwarm', edgecolor="white")
plt.text(-0.22,300, "B")
plt.text(1.18,300, "A")
plt.ylabel('weight, kg')
plt.xticks([-0.2, 1.2], ['Group 1', 'Group 2'])
plt.ylim(0, 300)

非常感谢！

Answer 1

Seaborn catplot是一个图形级绘图，它创建并占用一个新图形。要将这样的图作为子图，可以直接调用sns.barplot。提供ax会告诉该Barplot应该进入哪个子图。

barplot得到一个图例，在这种情况下是多余的，但是可以将其删除。

要只显示上方的误差线，可以在其顶部绘制误差线的矩形。大于错误栏（2）中各行的zorder的zorder可以解决此问题。

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

Treat1 = pd.DataFrame({'Treatment': 1, 'weight': np.random.randint(low=1, high=100, size=40)})
Treat2 = pd.DataFrame({'Treatment': 2, 'weight': np.random.randint(low=1, high=100, size=40)})
df1 = pd.concat([Treat1, Treat2])

Treat3 = pd.DataFrame({'Treatment': 1, 'weight': np.random.randint(low=100, high=300, size=40)})
Treat4 = pd.DataFrame({'Treatment': 2, 'weight': np.random.randint(low=100, high=300, size=40)})
df2 = pd.concat([Treat3, Treat4])

sns.set(style="ticks")
fig, axs = plt.subplots(ncols=2, figsize=(10, 4))

for ax, df, height in zip(axs, [df1, df2], [100, 300]):

    color_map = {1: "indianred", 2: "steelblue"}
    g = sns.barplot(x="Treatment", y="weight", hue="Treatment", capsize=.07, ci="sd",
                    data=df, palette=color_map, edgecolor="white", ax=ax)
    g.legend_.remove()
    for bar in g.patches:
        bar.set_zorder(3)
    ax.text(-0.2, height * 0.95, "B", ha='center')
    ax.text(1.2, height * 0.95, "A", ha='center')
    ax.set_ylabel('weight, kg')
    ax.set_xticks([-0.2, 1.2])
    ax.set_xticklabels(['Group 1', 'Group 2'])
    ax.set_ylim(0, height)

plt.tight_layout()
plt.show()