Pandas compare items in list in one column with single value in another column

Consider this two column df. I would like to create an apply function that compares each item in the "other_yrs" column list with the single integer in the "cur" column and keeps count of each item in the "other_yrs" column list that is greater than or equal to the single value in the "cur" column. I cannot figure out how to enable pandas to do this with apply. I am using apply functions for other purposes and they are working well. Any ideas would be very appreciated.

    cur other_yrs
1   11  [11, 11]
2   12  [16, 13, 12, 9, 9, 6, 6, 3, 3, 3, 2, 1, 0]
4   16  [15, 85]
5   17  [17, 17, 16]
6   13  [8, 8]

Below is the function I used to extract the values into the "other_yrs" column. I am thinking I can just insert into this function some way of comparing each successive value in the list with the "cur" column value and keep count. I really only need to store the count of how many of the list items are <= the value in the "cur" column.

def col_check(col_string):
cs_yr_lst = []
count = 0
if len(col_string) < 1:  #avoids col values of 0 meaning no other cases.
    pass
else:
    case_lst = col_string.split(", ")  #splits the string of cases into a list
    for i in case_lst:
        cs_yr = int(i[3:5])  #gets the case year from each individual case number
        cs_yr_lst.append(cs_yr)  #stores those integers in a list and then into a new column using apply
return cs_yr_lst

The expected output would be this:

  cur other_yrs    count
1   11  [11, 11]     2
2   12  [16, 13, 12, 9, 9, 6, 6, 3, 3, 3, 2, 1, 0]   11
4   16  [15, 85]     1
5   17  [17, 17, 16] 3
6   13  [8, 8]  2

for element in Dataframe1.Column1:
    
   Dataframe2[Dateframe2.Column2.isin([element])]

df['count'] = [np.sum(np.array(b) <= a) for a, b in zip(df['cur'], df['other_yrs'])]

df['count'] = pd.DataFrame(df['other_yrs'].tolist(), index=df.index).le(df['cur'], axis=0).sum(1)

   cur                                   other_yrs  count
1   11                                    [11, 11]      2
2   12  [16, 13, 12, 9, 9, 6, 6, 3, 3, 3, 2, 1, 0]     11
4   16                                    [15, 85]      1
5   17                                [17, 17, 16]      3
6   13                                      [8, 8]      2

u = df.explode('other_yrs')
df['Count'] = u['cur'].ge(u['other_yrs']).sum(level=0).astype(int)

print(df)
    cur                                   other_yrs  Count
1   11                                    [11, 11]      2
2   12  [16, 13, 12, 9, 9, 6, 6, 3, 3, 3, 2, 1, 0]     11
4   16                                    [15, 85]      1
5   17                                [17, 17, 16]      3
6   13                                      [8, 8]      2