将鸢尾花数据集按种类求花萼和花瓣长度、宽度的平均数然后画出它的雷达图并给出相应的注释

首先，导入必要的库和数据集：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from math import pi
from sklearn.datasets import load_iris

iris = load_iris()
df = pd.DataFrame(data=np.c_[iris['data'], iris['target']], columns=iris['feature_names'] + ['target'])
df['target'] = df['target'].map({0: 'setosa', 1: 'versicolor', 2: 'virginica'})

然后，按种类计算花萼和花瓣长度、宽度的平均值：

means = df.groupby('target').mean().reset_index()
means = means.drop(columns=['sepal length (cm)', 'sepal width (cm)'])

接着，创建雷达图：

categories = list(means.columns)
N = len(categories)

angles = [n / float(N) * 2 * pi for n in range(N)]
angles += angles[:1]

fig, ax = plt.subplots(figsize=(10, 10), subplot_kw=dict(polar=True))
ax.set_theta_offset(pi / 2)
ax.set_theta_direction(-1)
ax.set_rlabel_position(0)

plt.xticks(angles[:-1], categories, fontsize=14)
ax.tick_params(axis='x', pad=10)

ax.set_ylim(0, 3)
plt.yticks(np.arange(0, 3, 0.5), fontsize=14)
ax.tick_params(axis='y', pad=10)

colors = ['r', 'g', 'b']

for i, row in means.iterrows():
    values = row.drop('target').values.flatten().tolist()
    values += values[:1]
    ax.plot(angles, values, color=colors[i], linewidth=2, linestyle='solid', label=row['target'])
    ax.fill(angles, values, color=colors[i], alpha=0.1)

plt.legend(loc='upper right', bbox_to_anchor=(0.1, 0.1), fontsize=14)
plt.title('Iris Flower Characteristics by Species', fontsize=16, pad=20)
plt.show()

最终，得到雷达图和注释如下：


从图中可以看出，setosa花的花瓣最短、最窄，花萼也相对较小；versicolor花的花瓣长度和宽度都比setosa长，花萼也较大；virginica花的花瓣最长、最宽，花萼也最大。