pandas create a mask according to only the day and the month

I have the following dataframes:

dates,qq
1900-01-01,1
1900-01-02,2
1900-01-03,3
1900-01-04,1
1900-01-05,2
1901-01-06,5
1901-01-01,2
1901-01-02,2
1901-01-03,1
1901-01-04,4
1901-01-05,5
1901-01-06,6
1902-01-01,7
1902-01-02,1
1902-01-03,1
1902-01-04,2
1902-01-05,4
1902-01-06,5

and

dates,th
01-01,1
01-02,2
01-03,2
01-04,3
01-05,3
01-06,1

let's say dfr and dfr_t.

In the second one, dfr_t, some thresholds are stored.

I would like to compare the values of the first dataframe with the ones in the second (dfr_t) according to the day and month.
As you can notice indeed in the second one the dates column has only the month and the day while the year is non present.

I would like to know where a values of dfr with a specific day and month is less or equal to the threshold value defined in the second one (dfr_t) with the same day and month.

The following is the expected outcome:

1900-01-01	1	01-01	1	true
1900-01-02	2	01-02	2	true
1900-01-03	3	01-03	2	false
1900-01-04	1	01-04	3	true
1900-01-05	2	01-05	3	true
1900-01-06	5	01-06	1	false
1901-02-01	2	01-01	1	false
1901-02-02	2	01-02	2	true
1901-02-03	1	01-03	2	true
1901-02-04	4	01-04	3	false
1901-02-05	5	01-05	3	false
1901-02-06	6	01-06	1	false
1903-03-01	7	01-01	1	false
1903-03-02	1	01-02	2	true
1903-01-03	1	01-03	2	true
1903-01-04	2	01-04	3	true
1903-01-05	4	01-05	3	false
1903-01-06	5	01-06	1	false

As you can notice, for the date 01-01 the value corresponding to 1900-01-01 is equal to 1 (result=True); the value corresponding to 1901-01-01 is not equal\less to 1 (result=False); the value corresponding to 1902-01-01 is not equal\less to 1 (result=False).

I have read those dataframe as follow:

dfr   = pd.read_csv('test.csv', sep=',',index_col=0,parse_dates=True)

dfr_t = pd.read_csv('treh.csv', sep=',',index_col=0)

The first issue that I see is the following:

dfr_t.index.dtype
Out[15]: dtype('O')

I can not convert it to dateindex

ValueError: time data "1" doesn't match format "%m-%d", at position 0. You might want to try:
    - passing `format` if your strings have a consistent format;
    - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;
    - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this.

One idea could be duplicate dfr_t and concatenate it. This option, however, does not seem general.

The exact logic is unclear given the provided output, however you can probably merge on the month-day, then compare the values:

out = (dfr
  .merge(dfr_t, left_on=dfr.index.strftime('%m-%d'),
         right_index=True, how='left')
  .assign(flag=lambda d: d['qq'].le(d['th']))
)

Output:

    dates     dates_x  qq dates_y   th   flag
0   01-01  1900-01-01   1   01-01  1.0   True
1   01-02  1900-01-02   2   01-02  2.0   True
2   01-03  1900-01-03   3   01-03  2.0  False
3   01-04  1900-01-04   1   01-04  3.0   True
4   01-05  1900-01-05   2   01-05  3.0   True
5   01-06  1900-01-06   5   01-06  1.0  False
6   02-01  1900-02-01   2     NaN  NaN  False
7   02-02  1900-02-02   2     NaN  NaN  False
8   02-03  1900-02-03   1     NaN  NaN  False
9   02-04  1900-02-04   4     NaN  NaN  False
10  02-05  1900-02-05   5     NaN  NaN  False
11  02-06  1900-02-06   6     NaN  NaN  False
12  03-01  1900-03-01   7     NaN  NaN  False
13  03-02  1900-03-02   1     NaN  NaN  False
14  01-03  1900-01-03   1   01-03  2.0   True
15  01-04  1900-01-04   2   01-04  3.0   True
16  01-05  1900-01-05   4   01-05  3.0  False
17  01-06  1900-01-06   5   01-06  1.0  False

matching on the day only

out = (dfr
  .merge(dfr_t,
         left_on=pd.to_datetime(dfr['dates']).dt.strftime('%d'),
         right_on=dfr_t['dates'].str.extract(r'-(\d+)$', expand=False),
         how='left')
  .assign(flag=lambda d: d['qq'].le(d['th']))
)

Output:

   key_0     dates_x  qq dates_y  th   flag
0     01  1900-01-01   1   01-01   1   True
1     02  1900-01-02   2   01-02   2   True
2     03  1900-01-03   3   01-03   2  False
3     04  1900-01-04   1   01-04   3   True
4     05  1900-01-05   2   01-05   3   True
5     06  1900-01-06   5   01-06   1  False
6     01  1900-02-01   2   01-01   1  False
7     02  1900-02-02   2   01-02   2   True
8     03  1900-02-03   1   01-03   2   True
9     04  1900-02-04   4   01-04   3  False
10    05  1900-02-05   5   01-05   3  False
11    06  1900-02-06   6   01-06   1  False
12    01  1900-03-01   7   01-01   1  False
13    02  1900-03-02   1   01-02   2   True
14    03  1900-01-03   1   01-03   2   True
15    04  1900-01-04   2   01-04   3   True
16    05  1900-01-05   4   01-05   3  False
17    06  1900-01-06   5   01-06   1  False