Python Pandas：排序和分组，然后对第二列的两个连续行求和，得出第三列的特定值

Question

I have this dataframe:

    Group   Turn    Name
0   G1       1      Maria
1   G1       2      Sam
2   G1       2      Sara
3   G1       3      Maria
4   G1       4      Mark
5   G1       5      Maria

6   G2       2      Maria
7   G2       1      Ahmad

8   G3       1      Maria
9   G3       2      David

I would like to group by my data based on value of column "group" and sort based on their "Turn". So with each group the turns are sorted.

Then I would like to sum the value of column "Turn" in each group for the rows where the name is "Maria" and one row after. IF Maria is the last turn in the group then the sum only Maria's turn.

So the result looks like this:
    Group       Name    Sum 
        G1      Maria    3
        G1      Maria    7
        G1      Maria    5
        G2      Maria    2
        G3      Maria    3

I tried group by and apply and shift but none of them gives me the final result I am looking for.

 df = df.groupby('group').apply(lambda x: x.sort_values('Turn'))

Can somebody help me?

Answer 1

Use:

df.set_index(['Group','Name',(df['Name'] == 'Maria').cumsum().rename('Occurance')])\
  .sum(level=[0,2])\
  .reset_index()\
  .assign(name='Maria')\
  .drop('Occurance', axis=1)

Output:

  Group  Turn   name
0    G1     3  Maria
1    G1     7  Maria
2    G1     5  Maria
3    G2     3  Maria
4    G3     3  Maria

Answer 2

You can using ffill with limit

df=df.sort_values(['Group','Turn'])
df[df.Name.where(df.Name=='Maria').groupby(df['Group']).ffill(limit=1).eq('Maria')].set_index('Group').Turn.sum(level=0)
Out[272]: 
Group
G1    5
G2    3
G3    3
Name: Turn, dtype: int64