How to get a uniqueId for the following colum in pandas dataframe?-CodePudding

I have the following column in my pandas dataframe named as FailureLabel

   ID  FailureLabel       
0   1              1      
1   2              1  
2   3              1  
3   4              0       
4   5              0       
5   6              0  
6   7              0        
7   8              1        
8   9              1        
9   10             0
10  11             0
11  12             1     
12  13             1

I would like to assign a unique_id to this column such that eachs 1's have a unique id whereas all zeros the next one have a common "unique id".

I tried using the following code ,

df['unique_id'] = (df['FailureLabel'] | (df['FailureLabel']!=df['FailureLabel'].shift())).cumsum()

which gives me the following output,

   ID  FailureLabel       unique_id
0   1              1      1
1   2              1      2
2   3              1      3
3   4              0      4 
4   5              0      4
5   6              0      4
6   7              0      4 
7   8              1      5  
8   9              1      6  
9   10             0      7
10  11             0      7
11  12             1      8
12  13             1      9

But what I desire is,

   ID  FailureLabel       unique_id
0   1              1      1
1   2              1      2
2   3              1      3
3   4              0      4 
4   5              0      4
5   6              0      4
6   7              0      4 
7   8              1      4  
8   9              1      5  
9   10             0      6
10  11             0      6
11  12             1      6
12  13             1      7

CodePudding user response：

Use Series.shift with backfilling first value, compare by 1 and add cumulative sum:

df['unique_id'] = df['FailureLabel'].shift().bfill().eq(1).cumsum()

print (df)
    ID  FailureLabel  unique_id
0    1             1          1
1    2             1          2
2    3             1          3
3    4             0          4
4    5             0          4
5    6             0          4
6    7             0          4
7    8             1          4
8    9             1          5
9   10             0          6
10  11             0          6
11  12             1          6
12  13             1          7