Add row values for each groupby column in Pandas DataFrame

Question

I have a dataset that have binary values for flags for each sinid like this:

>>> df = pd.DataFrame({'sinid':['abc','def','ghi','abc','ghi'],'flag1':[1,1,0,0,1],'flag2':[1,0,1,0,0]})
>>> df
  sinid  flag1  flag2
0   abc      1      1
1   def      1      0
2   ghi      0      1
3   abc      0      0
4   ghi      1      0

I want to add values for each sinid, I think I need groupby but not sure how to use it...

This is the expected result:

  sinid  flag1  flag2
0   abc      1      1
1   def      1      0
2   ghi      1      1

`df.groupby('sinid').sum()`? – Quang Hoang May 31 '19 at 14:49 — Quang Hoang, May 31 '19 at 14:49

score 1 · Accepted Answer · answered May 31 '19 at 14:52

1

Group by then do a sum and reset the index.

df = df.groupby(['sinid']).sum().reset_index()
df

Result:

  sinid flag1   flag2
0   abc  1      1
1   def  1      0
2   ghi  1      1

answered May 31 '19 at 14:52

jose_bacoy

8,661
1
20
35

score 0 · Answer 2 · answered May 31 '19 at 14:50

0

Just summarize grouped dataframe:

df.groupby('sinid').sum()

    flag1   flag2
sinid       
abc     1   1
def     1   0
ghi     1   1

answered May 31 '19 at 14:50

vurmux

8,742
3
21
41

score 0 · Answer 3 · answered May 31 '19 at 14:51

0

This works:

df.groupby(['sinid'])['flag1', 'flag2'].sum().reset_index()

  sinid  flag1  flag2
0   abc      1      1
1   def      1      0
2   ghi      1      1

answered May 31 '19 at 14:51

Adarsh Chavakula

1,499
18
26

Add row values for each groupby column in Pandas DataFrame

3 Answers3