检查一列是否严格增加另一列的wrt

Question

我有以下数据帧df：

Subject Marks1 Marks2
English  1      10
English  1.5    20
English  1.7    30
English  3      40
Science  1      10
Science  1.5    20
Science  1.7    15
Science  3      35

我想按受试者分组，检查Marks2是否严格地随Marks1的增加而增加。如果没有，那么我想从df中删除该组，并将其放在另一个数据帧中。所以最后我会， df:

Subject Marks1 Marks2
English  1      10
English  1.5    20
English  1.7    30
English  3      40

问题：

Subject Marks1 Marks2
Science  1      10
Science  1.5    20
Science  1.7    15
Science  3      35

Answer 1

对所有列使用^{}with compare以获得较小的值，如0和^{}，然后获取主题的vals，并按^{}过滤输出：

m = df.groupby('Subject').diff().le(0).any(axis=1)

vals = df.loc[m, 'Subject']
mask = df['Subject'].isin(vals)
df1 = df[mask]
print (df1)
   Subject  Marks1  Marks2
4  Science     1.0      10
5  Science     1.5      20
6  Science     1.7      15
7  Science     3.0      35

df2 = df[~mask]
print (df2)
   Subject  Marks1  Marks2
0  English     1.0      10
1  English     1.5      20
2  English     1.7      30
3  English     3.0      40

编辑：每个组的瓶颈不同，如果所有组都已排序，则可以通过以下方式提高性能：

#columns used for difference (passed to groupby())
cols = ['Subject','col1','col2']
#sorting by all columns (if possible and if necessary)
df = df.sort_values(cols)
m = df[['Marks1','Marks2']].diff().le(0).any(axis=1) & df.duplicated(cols)

vals = df.loc[m, 'Subject']
mask = df['Subject'].isin(vals)
df1 = df[mask]

Answer 2

.filter()使用lambda函数查找.diff()以确定问题

 issues=df.groupby('Subject').filter(lambda x : ((x.Marks1.diff()>0)&(x.Marks2.diff()<0)).any())
    print(issues)


 Subject  Marks1  Marks2
4  Science     1.0      10
5  Science     1.5      20
6  Science     1.7      15
7  Science     3.0      35


Noissues=df[~df.index.isin(issues.index)]
print(Noissues)



  Subject  Marks1  Marks2
0  English     1.0      10
1  English     1.5      20
2  English     1.7      30
3  English     3.0      40