红斑鳞状疾病( ery - thematosus - squamous )的诊断在皮肤病科学领域是一个棘手的问题．因为它的不同种类的症状和表现都很类似．本题提供了一份皮肤病数据集［18)．数据 集中的每个样本包含22个组织病理学特征和12个临床特征．由于某些原因，数据集中的"年龄"( age ）特征存在缺失值．请使用均值填补法对其进行缺失值填补．实现可视化

首先，我们需要先加载数据集并查看缺失值情况：

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

df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/dermatology/dermatology.data', header=None)
df.columns = ['erythema', 'scaling', 'definite_borders', 'itching', 'koebner_phenomenon', 
              'polygonal_papules', 'follicular_papules', 'oral_mucosal_involvement', 
              'knee_and_elbow_involvement', 'scalp_involvement', 'family_history', 'melanin_incontinence', 
              'eosinophils_in_the_infiltrate', 'PNL_infiltrate', 'fibrosis_of_the_papillary_dermis', 
              'exocytosis', 'acanthosis', 'hyperkeratosis', 'parakeratosis', 'clubbing_of_the_rete_ridges', 
              'elongation_of_the_rete_ridges', 'thinning_of_the_suprapapillary_epidermis', 'spongiform_pustule', 
              'munro_microabcess', 'focal_hypergranulosis', 'disappearance_of_the_granular_layer', 
              'vacuolisation_and_damage_of_basal_layer', 'spongiosis', 'saw-tooth_appearance_of_retes', 
              'follicular_horn_plug', 'perifollicular_parakeratosis', 'inflammatory_monoluclear_inflitrate', 
              'band-like_infiltrate', 'Age', 'Class']

df['Age'] = df['Age'].replace('?', np.nan)
print('Missing values:\n', df.isnull().sum())

输出结果为：

Missing values:
 erythema                              0
scaling                               0
definite_borders                      0
itching                               0
koebner_phenomenon                    0
polygonal_papules                     0
follicular_papules                    0
oral_mucosal_involvement              0
knee_and_elbow_involvement            0
scalp_involvement                     0
family_history                        0
melanin_incontinence                  0
eosinophils_in_the_infiltrate         0
PNL_infiltrate                        0
fibrosis_of_the_papillary_dermis      0
exocytosis                            0
acanthosis                            0
hyperkeratosis                        0
parakeratosis                         0
clubbing_of_the_rete_ridges           0
elongation_of_the_rete_ridges         0
thinning_of_the_suprapapillary_epidermis  0
spongiform_pustule                    0
munro_microabcess                     0
focal_hypergranulosis                 0
disappearance_of_the_granular_layer   0
vacuolisation_and_damage_of_basal_layer  0
spongiosis                            0
saw-tooth_appearance_of_retes         0
follicular_horn_plug                  0
perifollicular_parakeratosis          0
inflammatory_monoluclear_inflitrate   0
band-like_infiltrate                  0
Age                                   8
Class                                 0
dtype: int64

我们可以发现，'Age'特征有8个缺失值。

接下来，我们使用均值填补法对缺失值进行填补：

age_mean = df['Age'].astype(float).mean()
df['Age'].fillna(age_mean, inplace=True)

最后，我们可以使用直方图可视化填补后的'Age'特征：

plt.hist(df['Age'], bins=10)
plt.xlabel('Age')
plt.ylabel('Count')
plt.title('Age Distribution')
plt.show()

输出结果为：


我们可以看到，填补后的'Age'特征呈现出了正态分布的趋势。