Get all the rows with max value in a pandas dataframe column in Python

I have a pandas dataframe with following columns.

col1  col2  col3   col4
A101  3     LLT    10028980
A101  7     LLT    10028980
A101  7     PT     10028980
A102  5     LLT    10028981
A102  3     PT     10028981 
A103  2     PT     10028982
A103  4     LLT    10028982

I would like to extract all those rows where col2 is max for each value of col1. The expected output is:

col1  col2  col3   col4
A101  7     LLT    10028980
A101  7     PT     10028980
A102  5     LLT    10028981
A103  4     LLT    10028982

I am using following lines of code but it is filtering the rows where there are multiple rows with max value (row 1 is excluded).

m = df.notnull().all(axis=1)

df = df.loc[m].groupby('col1').max().reset_index()

I am getting this output:

col1  col2  col3   col4
A101  7     PT     10028980
A102  5     LLT    10028981
A103  4     LLT    10028982

You can widen the maximum per group with transform, check equality against it to detect maximums and index with that mask:

>>> df.loc[df["col2"].eq(df.groupby("col1")["col2"].transform("max"))]

   col1  col2 col3      col4
1  A101     7  LLT  10028980
2  A101     7   PT  10028980
3  A102     5  LLT  10028981
6  A103     4  LLT  10028982

here's what .agg would do:

>>> df.groupby("col1")["col2"].agg("max")
col1
A101    7
A102    5
A103    4
Name: col2, dtype: int64

and what .transform does

>>> df.groupby("col1")["col2"].transform("max")

0    7
1    7
2    7
3    5
4    5
5    4
6    4
Name: col2, dtype: int64

which allows for an element-wise comparison with the col2 column.