python - 如何在 Pandas 中以分钟为单位获得时差

Question

我在 Pandas 中有以下数据框

  code     start_time      end_time
  0        13:00:09        13:30:09
  1        14:23:33        15:23:23
  2        11:30:00        12:30:00

start_time 和 end_time 是对象类型。

我想在几分钟内得到这两列的差异。我在 Pandas 中做以下事情

df['time_diff'] = pd.Timestamp(df['start_time']) - pd.Timestamp(df['end_time'])   

Answer 1

按 to_datetime 将列转换为日期时间或通过 to_timedelta 到 timedeltas , 减去 sub , 将输出时间增量转换为 total_seconds并划分 60:

start_time = pd.to_datetime(df['start_time'].astype(str)) 
end_time = pd.to_datetime(df['end_time'].astype(str))

#another solution
#start_time = pd.to_timedelta(df['start_time'].astype(str)) 
#end_time = pd.to_timedelta(df['end_time'].astype(str))

df['time_diff'] = start_time.sub(end_time).dt.total_seconds().div(60)
print (df)
   code start_time  end_time  time_diff
0     0   13:00:09  13:30:09 -30.000000
1     1   14:23:33  15:23:23 -59.833333
2     2   11:30:00  12:30:00 -60.000000

如果交换结束时间和开始时间:

df['time_diff'] = end_time.sub(start_time).dt.total_seconds().div(60)
print (df)
   code start_time  end_time  time_diff
0     0   13:00:09  13:30:09  30.000000
1     1   14:23:33  15:23:23  59.833333
2     2   11:30:00  12:30:00  60.000000

示例:

import datetime

df = pd.DataFrame({'code': [0, 1, 2], 
                  'start_time': [datetime.time(13, 0, 9), datetime.time(14, 23, 33), 
                                 datetime.time(11, 30)], 
                   'end_time': [datetime.time(13, 30, 9), datetime.time(15, 23, 23),
                                datetime.time(12, 30)]})

print (df)
   code start_time  end_time
0     0   13:00:09  13:30:09
1     1   14:23:33  15:23:23
2     2   11:30:00  12:30:00

IDE 来自评论，感谢@Anton vBR:

start_time = pd.to_timedelta(df['start_time'].astype(str)).dt.total_seconds()
end_time = pd.to_timedelta(df['end_time'].astype(str)).dt.total_seconds()

df['time_diff'] = end_time.sub(start_time).div(60)
print (df)
   code start_time  end_time  time_diff
0     0   13:00:09  13:30:09  30.000000
1     1   14:23:33  15:23:23  59.833333
2     2   11:30:00  12:30:00  60.000000