如何创建带有子图和子图的图
Duk*_*Adl 1 python data-visualization matplotlib subplot seaborn
我正在用Python学习乳腺癌分类数据集。我正在尝试为每个特征绘制直方图，如何将这些直方图分为三组？就像下面的截图一样：
我想要实现的目标
这是我使用的代码：
from sklearn.datasets import load_breast_cancer # sample data
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = load_breast_cancer()
# Turn the feature data into a dataframe
df = pd.DataFrame(data.data, columns = data.feature_names)
# Add the target columns, and fill it with the target data
df["target"] = data.target
# display(df.head())
mean radius mean texture mean perimeter mean area mean smoothness mean compactness mean concavity mean concave points mean symmetry mean fractal dimension radius error texture error perimeter error area error smoothness error compactness error concavity error concave points error symmetry error fractal dimension error worst radius worst texture worst perimeter worst area worst smoothness worst compactness worst concavity worst concave points worst symmetry worst fractal dimension target
0 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 0
1 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 0
2 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 0
3 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 0
4 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 0
# plotting
plotnumber = 1
fig = plt.figure(figsize=(20, 20))
for column in df.drop('target', axis=1):
if plotnumber <= 30:
plt.subplot(5, 6, plotnumber)
sns.distplot(df[df['target'] == 0][column], label = 'malignant')
sns.distplot(df[df['target'] == 1][column], label = 'benign')
plt.legend()
plt.title(column)
plotnumber += 1
fig.tight_layout()
到目前为止我所拥有的
我想将它们分为三组：“平均”组、“错误”组和“最差”组。每组包括 10 个图（5 行，2 列）
这些并不能回答问题
- 如何绘制多个子图
- 不显示如何对子图进行分组
- Matplotlib 不同大小的子图
- 也没有显示如何对子图进行分组
- 这需要使用
matplotlib.figure.subfigures, 然后添加.subplots到每个子图。 - 在 中
seaborn v0.11.2，sns.distplot已被弃用并被sns.histplot. itertools.chain用于扁平化，但其他方法可以在如何从列表列表中制作扁平列表中groups找到？。- 测试于
python 3.10,pandas 1.4.2,matplotlib 3.5.1,seaborn 0.11.2- 如果您使用的是 Anaconda，则使用
conda update --all，否则使用 更新包pip。或者，使用适当的包版本创建新环境。
- 如果您使用的是 Anaconda，则使用
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_breast_cancer # sample data
from itertools import chain # to lazily flatten the nested list
# starting with the sample dataframe in the op
data = load_breast_cancer()
df = pd.DataFrame(data.data, columns=data.feature_names)
df["target"] = data.target
# change the target name to what should be in the legend
df.target = df.target.map({0: 'Malignant', 1: 'Benign'})
# create the groups of column names for each set of subplots
col_groups = [df.columns[df.columns.str.contains(v)] for v in ['mean', 'error', 'worst']]
# create the subfigures and subplots
fig = plt.figure(figsize=(20, 20), constrained_layout=True)
subfigs = fig.subfigures(1, 3, width_ratios=[1, 1, 1], wspace=.15)
axs0 = subfigs[0].subplots(5, 2)
axs0 = axs0.flatten()
subfigs[0].suptitle('Mean Values', fontsize=20)
axs1 = subfigs[1].subplots(5, 2)
axs1 = axs1.flatten()
subfigs[1].suptitle('Standard Error Values', fontsize=20)
axs2 = subfigs[2].subplots(5, 2)
axs2 = axs2.flatten()
subfigs[2].suptitle('Worst Values', fontsize=20)
# create a flattened list of tuples containing an axes and column name
groups = chain(*[list(zip(axes, group)) for axes, group in zip([axs0, axs1, axs2], col_groups)])
# iterate through each axe and column
for ax, col in groups:
sns.histplot(data=df, x=col, hue='target', kde=True, stat='density', ax=ax)
l = ax.get_legend() # remove this line to keep default legend
l.remove() # remove this line to keep default legend
# get the existing label text, otherwise use a custom list (e.g labels = ['Malignant', 'Benign'])
# remove this line to keep default legend
labels = [v.get_text() for v in l.get_texts()]
# add a single legend at the top of the figure; change loc and bbox_to_anchor to move the legend
# remove this line to keep default legend
fig.legend(title='Tumor Classification', handles=l.legendHandles, labels=labels, loc='lower center', ncol=2, bbox_to_anchor=(0.5, -0.03))
fig.suptitle('Breast Cancer Data', fontsize=30, y=1.05)
fig.savefig('test.png', bbox_inches="tight")
plt.show()
使用默认图例绘制
