I have a pandas Dataframe that looks similar to this:

For each ID in my df I want to take the second latest time_1 (if it exists). I then want to compare this time with the timestamps in time_2 and remove all rows from my df where time_2 is earlier than this time.
My expected output would be:

This problem is above my pandas level. I asked ChatGPT and this is the solution I got which in principle does what I want:

import pandas as pd

id = [101, 101, 101, 102, 102, 103, 103, 103]
time_1 = ['2024-06-20 14:32:22', '2024-06-20 15:21:31', '2024-06-20 15:21:31', '2024-06-20 16:26:51', '2024-06-20 16:26:51', '2024-06-20 20:05:44', '2024-06-20 22:41:22', '2024-06-20 23:11:56']
time_2 = ['2024-06-20 14:10:31', '2024-06-20 14:32:22', '2024-06-20 15:21:31', '2024-06-20 15:21:31', '2024-06-20 16:56:24', '2024-06-20 21:17:35', '2024-06-20 22:21:31', '2024-06-20 23:01:31']


df = pd.DataFrame({
    'id': id,
    'time_1': pd.to_datetime(time_1),
    'time_2': pd.to_datetime(time_2)
})

grouped = df.groupby('id')['time_1']
mask = pd.Series(False, index=data.index)

for id_value, group in data.groupby('id'):
    # Remove duplicates and sort timestamps
    unique_sorted_times = group['time_1'].drop_duplicates().sort_values()

    # Check if there's more than one unique time
    if len(unique_sorted_times) > 1:
        # Select the second last time
        second_last_time = unique_sorted_times.iloc[-2]
        # Update the mask for rows with time_2 greater than or equal to the second last time_1
        mask |= (data['id'] == id_value) & (data['time_2'] >= second_last_time)
    else:
        # If there's only one unique time, keep the row(s)
        mask |= (data['id'] == id_value)

filtered_data = data[mask]

My issue with this solution is the for-loop. This seems rather inefficient and my real data is quite large. And also I am curious if there is a better, more efficient solution for this.