Remove one row per sub-set conditionally

Goal: remove the biggest value (slowest time) from each subset (column Model) from Dataframe.

DataFrame:

    Model              Time
1   bert-base-uncased  6.570979
2   bert-base-uncased  11.570979
3   bert-base-uncased  6.788779
4   albert-large-v1    5.785576
5   albert-large-v1    5.603203
6   albert-large-v1    9.727373

Desired Dataframe:

    Model              Time
1   bert-base-uncased  6.570979
2   bert-base-uncased  6.788779
3   albert-large-v1    5.785576
4   albert-large-v1    5.603203

Code:

subsets = df['Model']
for s in subsets:
    df[s]

Please let me know if there’s anything else I can add to post.

>Solution :

Solution for remove all maximals values per groups by GroupBy.transform with max and compare for not equal by Series.ne, filter in boolean indexing:

print (df)
               Model       Time
1  bert-base-uncased   6.570979
2  bert-base-uncased  11.570979
3  bert-base-uncased   6.788779
2  bert-base-uncased  11.570979 <- added another maximum per bert-base-uncased 
4    albert-large-v1   5.785576
5    albert-large-v1   5.603203
6    albert-large-v1   9.727373

df1 = df[df['Time'].ne(df.groupby('Model')['Time'].transform('max'))]
print (df1)
               Model      Time
1  bert-base-uncased  6.570979
3  bert-base-uncased  6.788779
4    albert-large-v1  5.785576
5    albert-large-v1  5.603203

If need remove only first maximum add condition:

df2 = (df[df['Time'].ne(df.groupby('Model')['Time'].transform('max')) | 
          df['Time'].duplicated()])
print (df2)
               Model       Time
1  bert-base-uncased   6.570979
3  bert-base-uncased   6.788779
2  bert-base-uncased  11.570979
4    albert-large-v1   5.785576
5    albert-large-v1   5.603203