python - 数据集的每个变量的 seaborn.boxplot

Question

我正在使用 Wisconsin dataset 。要显示 Y 轴是数据帧变量(例如:radius_mean)且 X 轴是诊断的箱线图，我执行以下操作:

sns.boxplot(x='label', y='radius', data=dsWisconsin)

(dsWisconsin 是从 .csv 加载 pandas 的数据框)

我的问题是，我怎样才能显示每个变量的所有箱线图(在网格中)，而不是为每个变量执行前面的代码？

例如，类似这样的东西，但每个变量的箱线图来自威斯康星州:

         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  compactness_mean  concavity_mean  concave points_mean  symmetry_mean  fractal_dimension_mean  radius_se  texture_se  perimeter_se  area_se  smoothness_se  compactness_se  concavity_se  concave points_se  symmetry_se  fractal_dimension_se  radius_worst  texture_worst  perimeter_worst  area_worst  smoothness_worst  compactness_worst  concavity_worst  concave points_worst  symmetry_worst  fractal_dimension_worst
0    842302         M        17.99         10.38          122.80     1001.0          0.11840           0.27760          0.3001              0.14710         0.2419                 0.07871     1.0950      0.9053         8.589   153.40       0.006399         0.04904       0.05373            0.01587      0.03003              0.006193         25.38          17.33           184.60      2019.0            0.1622             0.6656           0.7119                0.2654          0.4601                  0.11890
1    842517         M        20.57         17.77          132.90     1326.0          0.08474           0.07864          0.0869              0.07017         0.1812                 0.05667     0.5435      0.7339         3.398    74.08       0.005225         0.01308       0.01860            0.01340      0.01389              0.003532         24.99          23.41           158.80      1956.0            0.1238             0.1866           0.2416                0.1860          0.2750                  0.08902
2  84300903         M        19.69         21.25          130.00     1203.0          0.10960           0.15990          0.1974              0.12790         0.2069                 0.05999     0.7456      0.7869         4.585    94.03       0.006150         0.04006       0.03832            0.02058      0.02250              0.004571         23.57          25.53           152.50      1709.0            0.1444             0.4245           0.4504                0.2430          0.3613                  0.08758
3  84348301         M        11.42         20.38           77.58      386.1          0.14250           0.28390          0.2414              0.10520         0.2597                 0.09744     0.4956      1.1560         3.445    27.23       0.009110         0.07458       0.05661            0.01867      0.05963              0.009208         14.91          26.50            98.87       567.7            0.2098             0.8663           0.6869                0.2575          0.6638                  0.17300
4  84358402         M        20.29         14.34          135.10     1297.0          0.10030           0.13280          0.1980              0.10430         0.1809                 0.05883     0.7572      0.7813         5.438    94.44       0.011490         0.02461       0.05688            0.01885      0.01756              0.005115         22.54          16.67           152.20      1575.0            0.1374             0.2050           0.4000                0.1625          0.2364                  0.07678

Answer 1

您可以将数据转换为整齐的格式并使用 FacetGrid

df = df.melt(id_vars=['id', 'diagnosis'])
df[:3]
#          id diagnosis     variable  value
# 0    842302         M  radius_mean  17.99
# 1    842517         M  radius_mean  20.57
# 2  84300903         M  radius_mean  19.69

cols = ['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean']
grid = sns.axisgrid.FacetGrid(df[df.variable.isin(cols)], col='variable', sharey=False)
grid.map(sns.boxplot, 'diagnosis','value')