更改熊猫箱线图的面颜色

虽然我仍然推荐海产的和未加工的matplotlib,而不是熊猫的绘图界面,但事实证明,您可以通过 patch_artist=True 作为一个笨蛋 df.plot.box ,将其作为kwarg传递给 df.plot ,这将作为一个夸格 matplotlib.Axes.boxplot .

import pandas as pd
import numpy as np
data = np.random.randn(100, 4)
labels = list("ABCD")
df = pd.DataFrame(data, columns=labels)
props = dict(boxes="DarkGreen", whiskers="DarkOrange", medians="DarkBlue", caps="Gray")
df.plot.box(color=props, patch_artist=True)

如前所述,我最终创建了一个函数来绘制此图,使用原始 matplotlib .

def plot_boxplot(data, ax):
    bp = ax.boxplot(data.values, patch_artist=True)

    for box in bp['boxes']:
        box.set(color='DarkGreen')
        box.set(facecolor='DarkGreen')

    for whisker in bp['whiskers']:
        whisker.set(color="DarkOrange")

    for cap in bp['caps']:
        cap.set(color="Gray")

    for median in bp['medians']:
        median.set(color="white")

    ax.axhline(0, color="DarkBlue", linestyle=":")

    ax.set_xticklabels(data.columns)

我建议使用 df.plot.box 具有 patch_artist=True 和 return_type='both' (它返回绘制箱线图的matplotlib轴和值为箱线图matplotlab行的字典),以便进行最佳定制。

例如,给定此数据:

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

df = pd.DataFrame(
    data=np.random.randn(100, 4), 
    columns=list("ABCD")
)

可以为设置特定颜色 所有的盒子 :

fig,ax = plt.subplots(figsize=(9,6)) 
ax,props = df.plot.box(patch_artist=True, return_type='both', ax=ax)
for patch in props['boxes']:
    patch.set_facecolor('lime')
plt.show()

可以为设置特定颜色 每个盒子 :

colors = ['green','blue','yellow','red']

fig,ax = plt.subplots(figsize=(9,6)) 
ax,props = df.plot.box(patch_artist=True, return_type='both', ax=ax)
for patch,color in zip(props['boxes'],colors):
    patch.set_facecolor(color)
plt.show()

颜色表 :

colors = np.random.randint(0,10, 4)
cm = plt.cm.get_cmap('rainbow')
colors_cm = [cm((c-colors.min())/(colors.max()-colors.min())) for c in colors]

fig,ax = plt.subplots(figsize=(9,6)) 
ax,props = df.plot.box(patch_artist=True, return_type='both', ax=ax)
for patch,color in zip(props['boxes'],colors_cm):
    patch.set_facecolor(color)
# to add colorbar
fig.colorbar(plt.cm.ScalarMappable(
    plt.cm.colors.Normalize(min(colors),max(colors)), 
    cmap='rainbow'
), ax=ax, cmap='rainbow')
plt.show()