Remove rows with dates older than "today"

Question

I have been searching around here and google and have found many examples that show how to remove old dates based on a non moving date variable but can not figure out how to remove rows of data based on today's date going forward. In the example below, how would I go about removing anything older than today's date (should be one value removed) then save it? The real source file will continue to get new data everyday and I will need to be removing everything older than 'today' date.

from datetime import datetime

import pandas as pd

data = {'date': ['2001-04-10 18:47:05.069722', '2018-05-16 18:47:05.119994', '2018-05-16 18:47:05.178768', '2018-05-16 18:47:05.230071', '2018-05-16 18:47:05.230071', '2018-05-16 18:47:05.280592', '2018-05-16 18:47:05.332662', '2018-05-16 18:47:05.385109', '2018-05-16 18:47:05.436523', '2018-05-16 18:47:05.486877'], 
        'battle_deaths': [34, 25, 26, 15, 15, 14, 26, 25, 62, 41]}

df = pd.DataFrame(data, columns = ['date', 'battle_deaths'])

df
                         date  battle_deaths
0  2001-04-10 18:47:05.069722             34
1  2018-05-16 18:47:05.119994             25
2  2018-05-16 18:47:05.178768             26
3  2018-05-16 18:47:05.230071             15
4  2018-05-16 18:47:05.230071             15
5  2018-05-16 18:47:05.280592             14
6  2018-05-16 18:47:05.332662             26
7  2018-05-16 18:47:05.385109             25
8  2018-05-16 18:47:05.436523             62
9  2018-05-16 18:47:05.486877             41

Answer 1

Pandas has spared no expense to make life easier for developers. Compare against to_datetime('today') and filter accordingly:

df[pd.to_datetime(df.date, errors='coerce') >= pd.to_datetime('today')]

                         date  battle_deaths
1  2018-05-16 18:47:05.119994             25
2  2018-05-16 18:47:05.178768             26
3  2018-05-16 18:47:05.230071             15
4  2018-05-16 18:47:05.230071             15
5  2018-05-16 18:47:05.280592             14
6  2018-05-16 18:47:05.332662             26
7  2018-05-16 18:47:05.385109             25
8  2018-05-16 18:47:05.436523             62
9  2018-05-16 18:47:05.486877             41

This removes the 0^th row.