Question

我希望保留数据集中的重复项，使其保留最高值。现在我正在使用熊猫：

c_maxes = hospProfiling.groupby(['Hospital_ID', 'District_ID'], group_keys=False)\
                .apply(lambda x: x.ix[x['Hospital_employees'].idxmax()])
print c_maxes

c_maxes.to_csv('data/external/HospitalProfilingMaxes.csv')

执行此操作会导致初始数据集：Hospital_ID,District_ID,Hospital_employees成为Hospital_ID,District_ID,Hospital_ID,District_ID,Hospital_employees。

用于分组的列正在重复。这里的错误是什么？

编辑：

使用groupby（）函数时，会在数据开头添加一个额外的列。该列没有名称，它只是所有行的序列号。这在问题的输出第二个答案中显示。我想删除这个额外的列，因为我不需要它。我试过这个：

hospProfiling.drop(hospProfiling.columns[0], axis=1)

此代码不会删除列。如何删除它？

Answer 1

为什么不使用groupby max方法？

hopsProfiling.groupby(['Hospital_ID','District_ID'],as_index = False).max()

如果碰巧有三列以上，请用agg替换max：

hopsProfiling.groupby(['Hospital_ID','District_ID'],as_index = False).agg({'Hospital employees': max})

Answer 2

我认为你需要：

hospProfiling.loc[hospProfiling.groupby(['Hospital_ID', 'District_ID'])['Hospital_employees']
                               .idxmax()]

我对另一个答案感到非常惊讶，如果函数idxmax没用，我做了一些研究：

样品：

hospProfiling = pd.DataFrame({'Hospital_ID': {0: 'A', 1: 'A', 2: 'B', 3: 'A', 4: 'A', 5: 'B', 6: 'A', 7: 'A', 8: 'B', 9: 'B', 10: 'A', 11: 'B', 12: 'A'}, 'Name': {0: 'Sam', 1: 'Annie', 2: 'Fred', 3: 'Sam', 4: 'Annie', 5: 'Fred', 6: 'Sam', 7: 'Annie', 8: 'Fred', 9: 'James', 10: 'Alan', 11: 'Julie', 12: 'Greg'}, 'District_ID': {0: 'M', 1: 'F', 2: 'M', 3: 'M', 4: 'F', 5: 'M', 6: 'M', 7: 'F', 8: 'M', 9: 'M', 10: 'M', 11: 'F', 12: 'M'}, 'Hospital_employees': {0: 25, 1: 41, 2: 70, 3: 44, 4: 12, 5: 14, 6: 20, 7: 10, 8: 30, 9: 18, 10: 56, 11: 28, 12: 33}, 'Val': {0: 100, 1: 7, 2: 14, 3: 200, 4: 5, 5: 20, 6: 1, 7: 0, 8: 7, 9: 9, 10: 6, 11: 9, 12: 47}})
hospProfiling = hospProfiling[['Hospital_ID','District_ID','Hospital_employees','Val','Name']]
hospProfiling.sort_values(by=['Hospital_ID','District_ID'], inplace=True)
print (hospProfiling)
   Hospital_ID District_ID  Hospital_employees  Val   Name
1            A           F                  41    7  Annie
4            A           F                  12    5  Annie
7            A           F                  10    0  Annie
0            A           M                  25  100    Sam
3            A           M                  44  200    Sam
6            A           M                  20    1    Sam
10           A           M                  56    6   Alan
12           A           M                  33   47   Greg
11           B           F                  28    9  Julie
2            B           M                  70   14   Fred
5            B           M                  14   20   Fred
8            B           M                  30    7   Fred
9            B           M                  18    9  James

主要区别在于如何处理其他列，如果使用max它会返回每列的最大值 - 此处为Hospital_employees和Val：

c_maxes = hospProfiling.groupby(['Hospital_ID','District_ID'],as_index = False).max()
print (c_maxes)
  Hospital_ID District_ID  Hospital_employees   Name  Val
0           A           F                  41  Annie    7
1           A           M                  56    Sam  200
2           B           F                  28  Julie    9
3           B           M                  70  James   20

c_maxes = hospProfiling.groupby(['Hospital_ID','District_ID'],as_index = False)
                       .agg({'Hospital_employees': max})
print (c_maxes)
  Hospital_ID District_ID  Hospital_employees
0           A           F                  41
1           A           M                  56
2           B           F                  28
3           B           M                  70

函数idxmax返回另一列中最大值的索引：

print (hospProfiling.groupby(['Hospital_ID', 'District_ID'])['Hospital_employees'].idxmax())
A            F               1
             M              10
B            F              11
             M               2
Name: Hospital_employees, dtype: int64

然后您只能loc选择DataFrame：

c_maxes = hospProfiling.loc[hospProfiling.groupby(['Hospital_ID', 'District_ID'])['Hospital_employees']
                       .idxmax()]
print (c_maxes)
   District_ID Hospital_ID  Hospital_employees   Name  Val
1            F           A                  41  Annie    7
10           M           A                  56   Alan    6
11           F           B                  28  Julie    9
2            M           B                  70   Fred   14

如何删除数据集中的重复值：python

2 个答案: