根据条件在 df 的新列中添加值

Question

I have the following df sorted by date and the by name :

         date     name   valor 
  2  2018-03-01   ACC      75      
  0  2018-03-01   ACE      50     
  0  2018-03-20   ACE      50   
  1  2018-03-01   BBV      20    
  1  2018-03-14   BBV      20
  5  2018-04-16   BBV      58
  6  2018-04-20   BBV     -58

I am looking forward to generate a new column (called result )in the df where if one of the values in name is the same as the one after , then add them together in the new column.

The desired output would look something like this:

     date        name    valor  result 
  2  2018-03-01   ACC      75     75
  0  2018-03-01   ACE      50     50
  0  2018-03-20   ACE      50    100 
  1  2018-03-01   BBV      20     20
  1  2018-03-14   BBV      20     40
  5  2018-04-16   BBV      58     98
  6  2018-04-20   BBV     -58     40 

This is what I am trying:

for index,row in df.iterrows():
    for i in range(1,len(df)+1):
        if (row['name'][i]==row['name'][i+1]) and ( row['name'][i-1]!=row['name'][i]):
            df["result"]=df["valor"][i]+df["valor"][i+1]
        elif (row['name'][i]==row['name'][i+1]) and (row['name'][i-1]==row['name'][i]):
            df["result"]=df["result"][i]+df["valor"][i+1]

An indexing error outputs indicating string index out of range , however I am sure there should be a more efficient way to obtain the desired output.

Thank you for reading my post.

Answer 1

You should use groupby.cumsum for this. Using vectorised functionality which comes with pandas is usually more efficient and cleaner than iterating rows.

df['result'] = df.groupby('name')['valor'].cumsum()

print(df)

         date name  valor  result
2  2018-03-01  ACC     75      75
0  2018-03-01  ACE     50      50
0  2018-03-20  ACE     50     100
1  2018-03-01  BBV     20      20
1  2018-03-14  BBV     20      40
5  2018-04-16  BBV     58      98
6  2018-04-20  BBV    -58      40