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))
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
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