id         action
date
1900-11-01 00:00:00  10.0    starts_game
1900-11-01 00:05:00  10.0  team_a_scores
1900-11-01 00:25:00  10.0  team_a_scores
1900-11-01 00:30:00  10.0  team_a_scores
1900-11-01 00:55:00  10.0  team_b_scores
1900-11-01 23:58:00  99.0    starts_game
1900-11-02 00:40:00  99.0  team_b_scores
1900-11-02 00:50:00  99.0  team_b_scores
1900-11-03 00:05:00  10.0    starts_game
1900-11-03 00:24:00  10.0  team_b_scores

data.resample('T').ffill().head()

                       id       action
date
1900-11-01 00:00:00  10.0  starts_game
1900-11-01 00:01:00  10.0  starts_game
1900-11-01 00:02:00  10.0  starts_game
1900-11-01 00:03:00  10.0  starts_game
1900-11-01 00:04:00  10.0  starts_game

data.resample('T').agg(dict(id='ffill', action=lambda _: 'playing')).head()



                       id   action
date
1900-11-01 00:00:00  10.0  playing
1900-11-01 00:01:00   NaN  playing
1900-11-01 00:02:00   NaN  playing
1900-11-01 00:03:00   NaN  playing
1900-11-01 00:04:00   NaN  playing

date,id,action
1900-11-01 00:00:00,10.0,starts_game
1900-11-01 00:05:00,10.0,team_a_scores
1900-11-01 00:25:00,10.0,team_a_scores
1900-11-01 00:30:00,10.0,team_a_scores
1900-11-01 00:55:00,10.0,team_b_scores
1900-11-01 23:58:00,99.0,starts_game
1900-11-02 00:40:00,99.0,team_b_scores
1900-11-02 00:50:00,99.0,team_b_scores
1900-11-03 00:05:00,10.0,starts_game
1900-11-03 00:24:00,10.0,team_b_scores

import pandas as pd


filename = 'your_custom_name.csv'
data = pd.read_csv(filename)

data = data.set_index('date')