Question

这是以下问题的后续行动：Pandas DataFrame Window Function

  analysis  first_pass   fruit  order  second_pass  test units highest  \
0     full        12.1   apple      2         20.1     1     g    True   
1     full         7.1   apple      1         12.0     2     g   False   
2  partial        14.3   apple      3         13.1     1     g   False   
3     full        20.1  orange      2         20.1     1     g    True   
4     full        17.1  orange      1         18.5     2     g    True   
5  partial        23.4  orange      3         22.7     1     g    True   
6     full        23.1   grape      3         14.1     1     g   False   
7     full        17.2   grape      2         17.1     2     g   False   
8  partial        19.1   grape      1         19.4     1     g   False   

     highest_fruit        
0  [apple, orange] 
1         [orange] 
2         [orange] 
3  [apple, orange]   
4         [orange]  
5         [orange] 
6  [apple, orange]   
7         [orange] 
8         [orange]

在最初的问题中，我被引导到上表，其中通过对表格进行转换来指示给定分析和测试组合的最高水果（例如完整分析 测试1 导致苹果和橙子水果具有最高的第二次传球次数）。

我现在正试图利用这些信息来计算第一次传球的相对表现。例如，现在我知道苹果和橙色是完整分析，测试1 的最高成果，我想知道他们是否在第一次通过时有所改善。（苹果在第二次传球时得分为20.1，而在第一次传球时得分为12.1;在第一次传球得分为19.1之后，同样橙色提高到20.1）。

我想要一张类似下面的表（1 =改进，0 =没有变化，-1更差）：

  analysis  first_pass   fruit  order  second_pass  test units highest  \
0     full        12.1   apple      2         20.1     1     g    True   
1     full         7.1   apple      1         12.0     2     g   False   
2  partial        14.3   apple      3         13.1     1     g   False   
3     full        20.1  orange      2         20.1     1     g    True   
4     full        17.1  orange      1         18.5     2     g    True   
5  partial        23.4  orange      3         22.7     1     g    True   
6     full        23.1   grape      3         14.1     1     g   False   
7     full        17.2   grape      2         17.1     2     g   False   
8  partial        19.1   grape      1         19.4     1     g   False   

     highest_fruit        score_change_between_passes
0  [apple, orange]       {"apple" : 1, "orange" : 0}
1         [orange]       {"orange" : 1}
2         [orange]       {"orange" : -1}
3  [apple, orange]       {"apple" : 1, "orange" : 0}
4         [orange]       {"orange" " 1}
5         [orange]       {"orange" : -1}
6  [apple, orange]       {"apple" : 1, "orange" : 0}
7         [orange]       {"orange" : 1}
8         [orange]       {"orange" : -1}

Answer 1

您可以使用np.sign()：

second_pass = df.groupby(['test', 'analysis']).apply(lambda x: {fruit: int(np.sign(x.loc[x.fruit==fruit, 'second_pass'].iloc[0] - x.loc[x.fruit==fruit, 'first_pass'].iloc[0])) for fruit in x.highest_fruit.iloc[0]}).reset_index()
df = df.merge(second_pass, on=['test', 'analysis'], how='left').rename(columns={0: 'second_pass_comp'})


  analysis  first_pass   fruit  order  second_pass  test units highest  \
0     full        12.1   apple      2         20.1     1     g    True   
1     full         7.1   apple      1         12.0     2     g   False   
2  partial        14.3   apple      3         13.1     1     g   False   
3     full        19.1  orange      2         20.1     1     g    True   
4     full        17.1  orange      1         18.5     2     g    True   
5  partial        23.4  orange      3         22.7     1     g    True   
6     full        23.1   grape      3         14.1     1     g   False   
7     full        17.2   grape      2         17.1     2     g   False   
8  partial        19.1   grape      1         19.4     1     g   False   

     highest_fruit         first_pass_highest_fruit           second_pass_comp  
0  [apple, orange]  {'orange': 19.1, 'apple': 12.1}  {'orange': 1, 'apple': 1}  
1         [orange]                 {'orange': 17.1}              {'orange': 1}  
2         [orange]                 {'orange': 23.4}             {'orange': -1}  
3  [apple, orange]  {'orange': 19.1, 'apple': 12.1}  {'orange': 1, 'apple': 1}  
4         [orange]                 {'orange': 17.1}              {'orange': 1}  
5         [orange]                 {'orange': 23.4}             {'orange': -1}  
6  [apple, orange]  {'orange': 19.1, 'apple': 12.1}  {'orange': 1, 'apple': 1}  
7         [orange]                 {'orange': 17.1}              {'orange': 1}  
8         [orange]                 {'orange': 23.4}             {'orange': -1}

索引查找计算

1 个答案: