python - 找不到我的数据集的异常值(更具体地说是 IQR)

Question

尝试在 python 中使用 pandas 查找 Excel 工作表的异常值。我能够找到第一个和第三个四分位数，但无法在不返回 NaN 的情况下减去另一个四分位数。

这是基本代码:

absent = pd.read_excel('Absenteeism_at_work.xls')

print("\nOUTLIERS:")
# q1 = (absent.loc[:741, ['Distance from Residence to Work']].quantile([0.25]))
# q3 = (absent.loc[:741, ['Distance from Residence to Work']].quantile([0.75]))

#print(absent.loc[:741, 'Distance from Residence to Work'].quantile([0.25])) #quartile

#print(q1)
# q1, q3 = absent.loc[:741, ['Distance from Residence to Work', 'Transportation expense', 'Month of absence',
  #                       'Social smoker', 'Social drinker', 'Education']].quantile([0.25 - 0.75])

print(absent.loc[:741, ['Distance from Residence to Work', 'Transportation expense', 'Month of absence',
                      'Social smoker', 'Social drinker', 'Education']].quantile([0.75])
   - absent.loc[:741, ['Distance from Residence to Work', 'Transportation expense', 'Month of absence',
                    'Social smoker', 'Social drinker', 'Education']].quantile([0.25]))

输出:

OUTLIERS:
      Distance from Residence to Work  Transportation expense  \
0.25                              NaN                     NaN   
0.75                              NaN                     NaN   

      Month of absence  Social smoker  Social drinker  Education  
0.25               NaN            NaN             NaN        NaN  
0.75               NaN            NaN             NaN        NaN  

Answer 1

1. 您的代码只是简单的四分位数范围计算。如果它对你有用，那就太好了。如果您需要真正的异常值检测，这比基于四分位数的模式更复杂，尤其是多变量，您可以求助于 python 包，如 sklearn 或 pyod。

2. 使用分位数函数，您需要清理原始数据以确保它只是数字。特别是，您导入 Excel 文件作为数据源。

3. 检查数据

   tmp_df=absent.iloc[:741]

   cols = ['居住地到工作地点的距离', '交通费', “缺席的一个月”， “社交吸烟者”， “社交饮酒者”， ‘教育’]

   打印(tmp_df[col].quantile(0.25,0.75))

   打印(tmp_df[col].describe(include='all'))

祝你好运。

怀俄明州