特征锦囊：怎么把被错误填充的缺失值还原？

# 引入数据集(皮马印第安人糖尿病预测数据集)
pima_columns = ['times_pregment','plasma_glucose_concentration','diastolic_blood_pressure','triceps_thickness',
                'serum_insulin','bmi','pedigree_function','age','onset_disbetes']

pima = pd.read_csv('./data/pima.data', names=pima_columns)


# 处理被错误填充的缺失值0，还原为 空(单独处理)
pima['serum_insulin'] = pima['serum_insulin'].map(lambda x:x if x !=0 else None)
# 检查变量缺失情况
pima['serum_insulin'].isnull().sum()

# Output：374

# 批量操作 还原缺失值
columns = ['serum_insulin','bmi','plasma_glucose_concentration','diastolic_blood_pressure','triceps_thickness']

for col in columns:
    pima[col].replace([0], [None], inplace=True)

# 检查变量缺失情况
pima.isnull().sum()