大熊猫群中的列和

Question

下面是数据

Skill   Category    Location    Market Type Count
Java    Cat1        Europe      Tier1   A    2       
Java    Cat1        Europe      Tier1   B    1       
Java    Cat1        Europe      Tier1   C    1       
Java    Cat2        Asia        Tier2   D    1       
Java    Cat3        Asia        Tier1   E    1       

下面是预期的输出数据

Skill   Category    Location    Market Type Count   Sum_Market
Java    Cat1        Europe      Tier1   A    2       4
Java    Cat1        Europe      Tier1   B    1       4
Java    Cat1        Europe      Tier1   C    1       4
Java    Cat2        Asia        Tier2   D    1       1
Java    Cat3        Asia        Tier1   E    1       1

问题陈述:在这些选择中，Sum_Market应该使用特定技能、类别、位置和市场层次之和的组合来完成。下面是我最后的尝试：

df.groupby(['Skill','Category','Location','Market','Type'])['count'].sum()

Answer 1

使用

df['Sum_Market'] = df.groupby(['Skill','Category','Location'])['Count'].transform('sum')

OUTPUT

  Skill Category Location Market Type  Count  Sum_Market
0  Java     Cat1   Europe  Tier1    A      2           4
1  Java     Cat1   Europe  Tier1    B      1           4
2  Java     Cat1   Europe  Tier1    C      1           4
3  Java     Cat2     Asia  Tier2    D      1           1
4  Java     Cat3     Asia  Tier1    E      1           1

Answer 2

只是合并回到原来的：

df.merge(
df.groupby(['Skill','Category','Location','Market','Type'])['count'].sum().rename('Sum_Market').reset_index()
)