Python 数据框 - 基于列删除连续行

Question

I need to remove consecutive rows based on a column value. my dataframe looks like below

df = pd.DataFrame({
            "CustID":
                ["c1","c1","c1","c1","c1","c1","c1","c1","c1","c1","c2","c2","c2","c2","c2","c2"],
            "saleValue":
                [10, 12, 13, 6, 4 , 2, 11, 17, 1,5,8,2,16,13,1,4],
             "Status":
                [0, 0, 0, 1, 1 ,1, 0, 0, 1,1,1,1,0,0,1,1]
            
            
    })

dataframe looks like below

  CustID    saleValue   Status
    c1            10    0
    c1            12    0
    c1            13    0
    c1             6    1
    c1             4    1
    c1             2    1
    c1            11    0
    c1            17    0
    c1             1    1
    c1             5    1
    c2             8    1
    c2             2    1
    c2            16    0
    c2            13    0
    c2             1    1
    c2             4    1
    

I need to drop the consecutive rows for each CustID only when the Status is 1 .Can you please let me know the best way to do it

so the output should look like below.
 

CustID  saleValue   Status
    c1        10          0
    c1        12          0
    c1        13          0
    c1         6          1
    c1        11          0
    c1        17          0
    c1         1          1
    c2         8          1
    c2        16          0
    c2        13          0
    c2         1          1

Answer 1

Create a Boolean mask for the entire DataFrame.

Given the DataFrame is already grouped by ID, find rows where the value is 1, the previous row is also 1, and where the ID is the same as the ID on the previous row. These are the rows to drop, so keep the rest.

to_drop = (df['Status'].eq(1) & df['Status'].shift().eq(1)  # Consecutive 1s
           & df['CustID'].eq(df['CustID'].shift()))         # Within same ID  

df[~to_drop]

---

   CustID  saleValue  Status
0      c1         10       0
1      c1         12       0
2      c1         13       0
3      c1          6       1
6      c1         11       0
7      c1         17       0
8      c1          1       1
10     c2          8       1
12     c2         16       0
13     c2         13       0
14     c2          1       1