Python 如何使用PANAS根据DataFrame DF中的DATE_TIME行对df1中的距离列求和

mozway的答案已经令人满意了，但这是另一种方法.

import pandas as pd
from io import StringIO


def get_df(data):
    return pd.read_csv(
        StringIO(data), parse_dates=["date_time"], dayfirst=True
    )


if __name__ == "__main__":
    df1 = get_df(
        """IGN,External,Voltage,date_time
        0,ON,53.06,13-11-2023 10:01:14
        743,ON,47.80,13-11-2023 15:42:11
        745,ON,51.20,13-11-2023 16:22:41
        1152,ON,50.44,13-11-2023 19:18:26
        1155,ON,52.88,14-11-2023 09:39:54
        2191,ON,53.66,14-11-2023 14:45:53
        """
    )
    df2 = get_df(
        """IGN,External,Distance,date_time
        0,ON,1,13-11-2023 10:01:14
        300,ON,5,13-11-2023 10:42:11
        400,ON,6,13-11-2023 11:42:11
        743,ON,9,13-11-2023 15:42:11
        745,ON,10,13-11-2023 16:22:41
        1000,ON,12,13-11-2023 17:13:43
        1152,ON,89,13-11-2023 19:18:26
        1155,ON,52.88,14-11-2023 09:39:54
        1189,ON,54.33,14-11-2023 10:39:54
        2191,ON,34,14-11-2023 14:45:53
        """
    )

    group_times = zip(df1.date_time[:-1:2], df1.date_time[1::2])
    new_df = []
    for start_date, stop_date in group_times:
        new_df.append(
            {
                "start_date": start_date,
                "stop_date": stop_date,
                "distance": df2[
                    (start_date <= df2.date_time)
                    & (df2.date_time <= stop_date)
                ].Distance.sum(),
            }
        )
    result = pd.DataFrame(new_df)
    print(result)

    
    # OR:
    result = pd.DataFrame(
        [
            {
                "start_date": s1,
                "stop_date": s2,
                "distance": df2[
                    (s1 <= df2.date_time) & (df2.date_time <= s2)
                ].Distance.sum(),
            }
            for s1, s2 in zip(df1.date_time[:-1:2], df1.date_time[1::2])
        ]
    )