Please assist with the below

import pandas as pd
df = pd.DataFrame({'Grp': [1,1,1,1,2,2,2,2,3,3,3,4,4,4], 'Org1': ['x','x','y','y','z','y','z','z','x','y','y','z','x','x'], 'Org2': ['a','a','b','b','c','b','c','c','a','b','b','c','a','a'], 'Value': [0,0,3,1,0,1,0,5,0,0,0,1,1,1]})
df

*** I need the first non zero value having "FLAG" = 1 and other 0

Details :

For each unique set of "Grp, Org1, Org2" and based on the "Value" "FLAG" to have 1 and the others as 0.

If values are all 0 in a Column then FLAG = 0 for all

If values are all NON ZERO in a Column then first instance to have FLAG = 1 and others 0

I am expecting the output as below

+----+-----+------+------+-------+------+
|    | Grp | Org1 | Org2 | Value | FLAG |
+----+-----+------+------+-------+------+
|  0 |   1 | x    | a    |     0 |    0 |
|  1 |   1 | x    | a    |     0 |    0 |
|  2 |   1 | y    | b    |     3 |    1 |
|  3 |   1 | y    | b    |     1 |    0 |
|  4 |   2 | z    | c    |     0 |    0 |
|  5 |   2 | y    | b    |     1 |    1 |
|  6 |   2 | z    | c    |     0 |    0 |
|  7 |   2 | z    | c    |     5 |    1 |
|  8 |   3 | x    | a    |     0 |    0 |
|  9 |   3 | y    | b    |     0 |    0 |
| 10 |   3 | y    | b    |     0 |    0 |
| 11 |   4 | z    | c    |     1 |    1 |
| 12 |   4 | x    | a    |     1 |    1 |
| 13 |   4 | x    | a    |     1 |    0 |
+----+-----+------+------+-------+------+

Start with a simple flag to determine if the value is set.

df = df.assign(FLAG=df.Value.where(df.Value == 0, 1))
df
#     Grp Org1 Org2  Value  FLAG
# 0     1    x    a      0     0
# 1     1    x    a      0     0
# 2     1    y    b      3     1
# 3     1    y    b      1     1
# 4     2    z    c      0     0
# 5     2    y    b      1     1
# 6     2    z    c      0     0
# 7     2    z    c      5     1
# 8     3    x    a      0     0
# 9     3    y    b      0     0
# 10    3    y    b      0     0
# 11    4    z    c      1     1
# 12    4    x    a      1     1
# 13    4    x    a      1     1

Then, using groupby to work independently per group, you can find the first flag that was set by using pd.Series.cummax followed by pd.Series.diff.

flag = df.groupby(['Grp', 'Org1', 'Org2'])['FLAG'].transform(lambda x: x.cummax().diff())                                                                                                                                                    
df['FLAG'] = flag.where(flag.notnull(), df['FLAG']).astype(int)
df
#     Grp Org1 Org2  Value  FLAG
# 0     1    x    a      0     0
# 1     1    x    a      0     0
# 2     1    y    b      3     1
# 3     1    y    b      1     0
# 4     2    z    c      0     0
# 5     2    y    b      1     1
# 6     2    z    c      0     0
# 7     2    z    c      5     1
# 8     3    x    a      0     0
# 9     3    y    b      0     0
# 10    3    y    b      0     0
# 11    4    z    c      1     1
# 12    4    x    a      1     1
# 13    4    x    a      1     0

Using cummax will convert everything after the first 1 entry into a 1 as well, so that diff will be all 0 except for the first step from 0 to 1.