两个最主要的处理缺失值的方法是：
❏ 删除缺少值的行；
❏ 填充缺失值；

我们首先将serum_insulin的中的字段值0替换为None，可以看到缺失值的数量为374个；

print(pima['serum_insulin'].isnull().sum())
pima['serum_insulin'] = pima['serum_insulin'].map(lambda x:x if x != 0 else None)
print(pima['serum_insulin'].isnull().sum())
# 0
# 374

替换所有的缺失字段，可以看到不同字段缺失值的情况是不一样的；

columns = ['serum_insulin', 'bmi', 'plasma_glucose_concentration','diastolic_blood_pressure', 'triceps_thickness']
for c in columns:pima[c].replace([0], [None], inplace=True)print(pima.isnull().sum())
# times_pregnant                    0
# plasma_glucose_concentration      5
# diastolic_blood_pressure         35
# triceps_thickness               227
# serum_insulin                   374
# bmi                              11
# pedigree_function                 0
# age                               0
# onset_diabetes                    0
# dtype: int64

可以看到此时describe不会针对有缺失值的列进行计算

print(pima.describe())
#        times_pregnant  pedigree_function         age  onset_diabetes
# count      768.000000         768.000000  768.000000      768.000000
# mean         3.845052           0.471876   33.240885        0.348958
# std          3.369578           0.331329   11.760232        0.476951
# min          0.000000           0.078000   21.000000        0.000000
# 25%          1.000000           0.243750   24.000000        0.000000
# 50%          3.000000           0.372500   29.000000        0.000000
# 75%          6.000000           0.626250   41.000000        1.000000
# max         17.000000           2.420000   81.000000        1.000000

我们可以自己手动计算均值

# print(pima['plasma_glucose_concentration'].mean(), pima['plasma_glucose_concentration'].std())# 121.6867627785059 30.53564107280403

处理缺失数据最简单的方式就是丢弃数据行，我们使用dropna方法进行处理，可以看到将近丢弃一半的数据；从机器学习的角度考虑，尽管数据都有值、很干净，但是我们没有利用尽可能多的数据，忽略了一半以上的观察值。

pima_dropped = pima.dropna()
rows = pima.shape[0]
rows_dropped = pima_dropped.shape[0]
num_rows_lost = round(100*(rows-rows_dropped)/rows)
print('lost {}% rows'.format(num_rows_lost))
# lost 49% rows

通过以下我们可以看到糖尿病的患病概率影响并不是很大；

print(pima['onset_diabetes'].value_counts(normalize=True))
print(pima_dropped['onset_diabetes'].value_counts(normalize=True))
# onset_diabetes
# 0    0.651042
# 1    0.348958
# Name: proportion, dtype: float64
# onset_diabetes
# 0    0.668367
# 1    0.331633
# Name: proportion, dtype: float64

通过以下可以看到各个字段的均值处理前后的大小

pima_mean = pima.mean()
pima_dropped_mean = pima_dropped.mean()
print(pima_mean)
print(pima_dropped_mean)
# times_pregnant                    3.845052
# plasma_glucose_concentration    121.686763
# diastolic_blood_pressure         72.405184
# triceps_thickness                 29.15342
# serum_insulin                   155.548223
# bmi                              32.457464
# pedigree_function                 0.471876
# age                              33.240885
# onset_diabetes                    0.348958
# dtype: object# times_pregnant                     3.30102
# plasma_glucose_concentration    122.627551
# diastolic_blood_pressure         70.663265
# triceps_thickness                29.145408
# serum_insulin                   156.056122
# bmi                              33.086224
# pedigree_function                 0.523046
# age                              30.864796
# onset_diabetes                    0.331633
# dtype: object

可以看到进行数据处理之后，每个字段的变化率

mean_percent = (pima_dropped_mean - pima_mean) / pima_mean
print(mean_percent)
# times_pregnant                 -0.141489
# plasma_glucose_concentration    0.007731
# diastolic_blood_pressure       -0.024058
# triceps_thickness              -0.000275
# serum_insulin                   0.003265
# bmi                             0.019372
# pedigree_function               0.108439
# age                            -0.071481
# onset_diabetes                  -0.04965
# dtype: object

通过饼图查看各个字段的百分比变化；

ax = mean_percent.plot(kind='bar', title='% change in average column values')
ax.set_ylabel('% change')
plt.show()

可以看到，times_pregnant（怀孕次数）的均值在删除缺失值后下降了14%，变化很大！pedigree_function（糖尿病血系功能）也上升了11%，也是个飞跃。可以看到，删除行（观察值）会严重影响数据的形状，所以应该保留尽可能多的数据。

使用处理过的数据训练scikit-learn的K最近邻（KNN，k-nearest neighbor）分类模型，可以看到最好的邻居数是7个，此时KNN模型的准确率是74.5%；

from sklearn.neighbors import  KNeighborsClassifier
from sklearn.model_selection import  GridSearchCVX_dropped = pima_dropped.drop('onset_diabetes', axis= 1)
print('learning from {} rows'.format(X_dropped.shape[0]))
y_dropped = pima_dropped['onset_diabetes']knn_para = {'n_neighbors':[1,2,3,4,5,6,7]}
knn = KNeighborsClassifier()
grid = GridSearchCV(knn, knn_para)
grid.fit(X_dropped, y_dropped)
print(grid.best_score_, grid.best_params_)# learning from 392 rows
# 0.7348263550795197 {'n_neighbors': 7}