Python3.x Pandas：从 Pandas 数据框中的 1 和 0 模式中获取值和 ID 的计数

我有一个数据帧df，如下所示:

import pandas as pd

data = {'A': ['XYZ', 'XYZ', 'XYZ', 'XYZ', 'PQR', 'PQR', 'PQR', 'PQR', 'CVB', 'CVB', 'CVB', 'CVB'], 'B': ['2022-02-16 14:00:31', '2022-02-16 16:11:26', '2022-02-16 17:31:26',
                                                                                           '2022-02-16 22:47:46', '2022-02-17 07:11:11', '2022-02-17 10:43:36', 
                                                                                           '2022-02-17 15:05:11', '2022-02-18 18:06:12', '2022-02-19 09:05:46', 
                                                                                           '2022-02-19 13:02:16', '2022-02-19 18:05:26', '2022-02-19 22:05:26'], 'C': [1,0,0,0,1,0,1,0,0,0,0,1]}
df = pd.DataFrame(data)
df['B'] = pd.to_datetime(df['B'])
df


     |   A   |          B           |     C      |         
     +-------+----------------------+------------+
     |  XYZ  |  2022-02-16 14:00:31 |     1      |
     |  XYZ  |  2022-02-16 16:11:26 |     0      |
     |  XYZ  |  2022-02-16 17:31:26 |     0      |
     |  XYZ  |  2022-02-16 22:47:46 |     0      |
     |  PQR  |  2022-02-17 07:11:11 |     1      |
     |  PQR  |  2022-02-17 10:43:36 |     0      |
     |  PQR  |  2022-02-17 15:05:11 |     1      |
     +-------+----------------------+------------+

我想要实现的是对1和0的出现次数进行计数，并将计数值指定为DataFrame df的新列，并添加ID作为新列，以便预期的输出应如下所示.例如，在列C中，用于前四行的图案1,0,0,0的计数为4，并且类似地，在最后一行中仅存在计数为1的值1.

Expected Output :

     |   A   |          B           |     C      |  Count   |    ID   |            
     +-------+----------------------+------------+----------+---------+
     |  XYZ  |  2022-02-16 14:00:31 |     1      |     4    |  ABC_1  |
     |  XYZ  |  2022-02-16 16:11:26 |     0      |     NaN  |         |
     |  XYZ  |  2022-02-16 17:31:26 |     0      |     NaN  |         |
     |  XYZ  |  2022-02-16 22:47:46 |     0      |     NaN  |         |
     |  PQR  |  2022-02-17 07:11:11 |     1      |     2    |  ABC_2  | 
     |  PQR  |  2022-02-17 10:43:36 |     0      |     NaN  |         |
     |  PQR  |  2022-02-17 15:05:11 |     1      |     1    |  ABC_3  |
     +-------+----------------------+------------+----------+---------+

目前，我正试图通过使用下面的代码来实现相同的效果，但我无法获得预期的/期望的结果.

one_index = df[df['C'] == 1].index
zero_index = df[df['C'] == 0].index

df.loc[0, 'Count'] = len(df)
df.loc[one_index, 'ID'] = "ABC_1"

Actual Output :

     |   A   |          B           |     C      |  Count   |   ID   |                  
     +-------+----------------------+------------+----------+--------+
     |  XYZ  |  2022-02-16 14:00:31 |     1      |     7    |  ABC_1 |
     |  XYZ  |  2022-02-16 16:11:26 |     0      |    NaN   |        |
     |  XYZ  |  2022-02-16 17:31:26 |     0      |    NaN   |        |
     |  XYZ  |  2022-02-16 22:47:46 |     0      |    NaN   |        |
     |  PQR  |  2022-02-17 07:11:11 |     1      |    NaN   |  ABC_1 |
     |  PQR  |  2022-02-17 10:43:36 |     0      |    NaN   |        |
     |  PQR  |  2022-02-17 15:05:11 |     1      |    NaN   |  ABC_1 |
     +-------+----------------------+------------+----------+--------+

我如何计算Pandas 数据框中1和0的出现次数？