如果thread_opening已经存在，可以使用groupby.cumcount：

df['post_number'] = df.groupby(df['thread_opening'].cumsum()).cumcount()

如果你想要总数，只有当thread_opening是1时：

df['thread_comments_count'] = (df.groupby('thread_name')['thread_name']
                                 .transform('size')
                                 .where(df['thread_opening'].eq(1), 0)
                              )

输出：

user_name               thread_name post_text  post_number  thread_opening  thread_comments_count
0   BoxCutter  It's Okay When We Do It.     .....            1               1                      3
1   Docket_33  It's Okay When We Do It.     .....            2               0                      0
2   Hearmeout  It's Okay When We Do It.     .....            3               0                      0
3         yez                   Science     .....            1               1                      4
4  Mainländer                   Science     .....            2               0                      0
5        400z                   Science     .....            3               0                      0
6     Farnham                   Science     .....            4               0                      0

将numpy.where与Series.value_counts和Series.map一起使用：

df['thread_comments_count'] = np.where(df['thread_opening'].eq(1),
                                    df['thread_name'].map(df['thread_name'].value_counts()),
                                    0)
print (df)

     user_name                thread_name post_text  post_number  \
0   BoxCutter   It's Okay When We Do It.     .....             1   
1   Docket_33   It's Okay When We Do It.     .....             2   
2   Hearmeout   It's Okay When We Do It.     .....             3   
3         yez                    Science     .....             1   
4  Mainländer                    Science     .....             2   
5        400z                    Science     .....             3   
6     Farnham                    Science     .....             4   

   thread_opening  thread_comments_count  
0               1                      3  
1               0                      0  
2               0                      0  
3               1                      4  
4               0                      0  
5               0                      0  
6               0                      0