如何调整Seaborn的晶须尺寸？

Question

我想在下面的方框图中加宽晶须线。

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

data = pd.DataFrame({'Data': np.random.random(100), 'Type':['Category']*100})

fig, ax = plt.subplots()

# Plot boxplot setting the whiskers to the 5th and 95th percentiles
sns.boxplot(x='Type', y='Data', data=data, color = 'gray', whis = [5,95])

# Adjust boxplot and whisker line properties
for p, artist in enumerate(ax.artists):
    artist.set_edgecolor('blue')
    for q in range(p*6, p*6+6):
        line = ax.lines[q]
        line.set_color('pink')

我知道如何调整晶须的颜色和线宽，但我一直无法弄清楚如何增加晶须的长度。我最接近的尝试使用line.set_xdata([q/60-0.5, q/60+0.5])，但出现错误

ValueError: shape mismatch: objects cannot be broadcast to a single shape    

理想情况下，我希望晶须百分比线的宽度与盒子的宽度相同。我该怎么办？

Answer 1

您已经注意到，每个框有6条线（因此，您的p*6索引）。

索引为p*6+4的行具有框的宽度（这是框内的中线）。因此，我们可以使用它来设置其他线条的宽度。

要更改的行具有索引p*6+2和p*6+3。

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

data = pd.DataFrame({'Data': np.random.random(100), 'Type':['Category']*100})

fig, ax = plt.subplots()

# Plot boxplot setting the whiskers to the 5th and 95th percentiles
sns.boxplot(x='Type', y='Data', data=data, color = 'gray', whis = [5,95])

# Adjust boxplot and whisker line properties
for p, artist in enumerate(ax.artists):
    artist.set_edgecolor('blue')
    for q in range(p*6, p*6+6):
        line = ax.lines[q]
        line.set_color('pink')

    ax.lines[p*6+2].set_xdata(ax.lines[p*6+4].get_xdata())
    ax.lines[p*6+3].set_xdata(ax.lines[p*6+4].get_xdata())

这也适用于带有多个框的示例：

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

sns.set(style="whitegrid")
tips = sns.load_dataset("tips")
ax = sns.boxplot(x="day", y="total_bill", data=tips)

# Adjust boxplot and whisker line properties
for p, artist in enumerate(ax.artists):
    artist.set_edgecolor('blue')
    for q in range(p*6, p*6+6):
        line = ax.lines[q]
        line.set_color('pink')

    ax.lines[p*6+2].set_xdata(ax.lines[p*6+4].get_xdata())
    ax.lines[p*6+3].set_xdata(ax.lines[p*6+4].get_xdata())