How to correctly determine if a Pandas dataframe has replaced values in a column based on a string in another column

I have a really large Pandas dataframe in Python with three important columns; 'file', 'comment', and 'number'. It is a list of many different files with assigned id-numbers, but some of these files replaces old files and should have the same id-numbers instead of separate ones. An example is:

df_test = pd.DataFrame(data = None, columns = ['file','comment','number'])
df_test.file = ['file_1', 'file_1_v2', 'file_2', 'file_2_v2', 'file_3', 'file_3_v2']
df_test.comment = ['none', 'Replacing: file_1', 'none', 'Replacing: file_2', 'none', 'Replacing: file_3']
df_test.number = ['12', '12', '15', '16', '18', '18']

What I want is to check if the 'number' column shows the same id-number for the original file and the file which has a comment that starts with 'Replacing: ' that compares the number with the file shown at the end of the comment. In this example, I would want something like a list or a new column in the dataframe which reads; 'True', 'True', 'False', 'False', 'True', 'True'; since the second and last files have been assigned the same id-number as the file they are replacing, but the fourth file has not. I can't really figure out how to check it and any help is appreciated! Thanks!

If always none are before replacing files, is possible replace it to missing values and back filling missing values for helper Series of groups, last test if unique number per groups by GroupBy.transform with DataFrameGroupBy.nunique:

s = df_test['comment'].mask(df_test['comment'].eq('none')).bfill()
df_test['test'] = df_test.groupby(s)['number'].transform('nunique').eq(1)
print (df_test)

        file            comment number   test
0     file_1               none     12   True
1  file_1_v2  Replacing: file_1     12   True
2     file_2               none     15  False
3  file_2_v2  Replacing: file_2     16  False
4     file_3               none     18   True
5  file_3_v2  Replacing: file_3     18   True

Another idea for groups is extract filenames after last space in Series.str.extract with replace non matched values by Series.fillna and test uniquness per groups like previous solution:

s = df_test['comment'].str.extract(r'\s(.*)$', expand=False).fillna(df_test['file'])
df_test['test'] = df_test.groupby(s)['number'].transform('nunique').eq(1)
print (df_test)
        file            comment number   test
0     file_1               none     12   True
1  file_1_v2  Replacing: file_1     12   True
2     file_2               none     15  False
3  file_2_v2  Replacing: file_2     16  False
4     file_3               none     18   True
5  file_3_v2  Replacing: file_3     18   True