python 3.x - Filtering column value based on unique value, but not repeated for different value of the same column on the same unique value

Question

Looking for ways to filter unique values with inactive status, but not repeated as active status under the same unique value.

df:

Unique_value    Status
1               Active        <- Has both active and inactive, must be inactive only
1               Active        <- Has both active and inactive, must be inactive only
1               Inactive      <- Has both active and inactive, must be inactive only
1               Inactive      <- Has both active and inactive, must be inactive only
2               Inactive      <- Has inactive only
2               Inactive      <- Has inactive only
2               Inactive      <- Has inactive only
3               Inactive      <- Has inactive only (cancelled okay to be filtered out)
3               Cancelled     <- Has inactive only (cancelled okay to be filtered out)
3               Inactive      <- Has inactive only (cancelled okay to be filtered out)

Desired output:

Unique_value    status
2               Inactive
3               Inactive

What I tried so far, but I don't think this is correct.

p = ['Inactive', 'Active']
df.groupby('Unique_value')['Status'].apply(lambda x: (x =='Inactive') != set(p))

question from:https://stackoverflow.com/questions/65853855/filtering-column-value-based-on-unique-value-but-not-repeated-for-different-val

Answer 1

First check if any of the values in each group are Active or Inactive. Then get rid of the groups where both conditions are true:

m1 = df["Status"].eq("Active").groupby(df["Unique_value"]).transform("any")
m2 = df["Status"].eq("Inactive").groupby(df["Unique_value"]).transform("any")
df[~(m1 & m2)].groupby("Unique_value", as_index=False).first()

   Unique_value    Status
0             2  Inactive
1             3  Inactive