我需要0 days 08:00:00
到08:00:00
。
代码:
import pandas as pd
df = pd.DataFrame({
'Slot_no':[1,2,3,4,5,6,7],
'start_time':['0:01:00','8:01:00','10:01:00','12:01:00','14:01:00','18:01:00','20:01:00'],
'end_time':['8:00:00','10:00:00','12:00:00','14:00:00','18:00:00','20:00:00','0:00:00'],
'location_type':['not considered','Food','Parks & Outdoors','Food',
'Arts & Entertainment','Parks & Outdoors','Food']})
df = df.reindex_axis(['Slot_no','start_time','end_time','location_type','loc_set'], axis=1)
df['start_time'] = pd.to_timedelta(df['start_time'])
df['end_time'] = pd.to_timedelta(df['end_time'].replace('0:00:00', '24:00:00'))
输出:
print (df)
Slot_no start_time end_time location_type loc_set
0 1 00:01:00 0 days 08:00:00 not considered NaN
1 2 08:01:00 0 days 10:00:00 Food NaN
2 3 10:01:00 0 days 12:00:00 Parks & Outdoors NaN
3 4 12:01:00 0 days 14:00:00 Food NaN
4 5 14:01:00 0 days 18:00:00 Arts & Entertainment NaN
5 6 18:01:00 0 days 20:00:00 Parks & Outdoors NaN
6 7 20:01:00 1 days 00:00:00 Food NaN
答案 0 :(得分:1)
您可以to_datetime
使用dt.time
:
df['end_time_times'] = pd.to_datetime(df['end_time']).dt.time
print (df)
Slot_no start_time end_time location_type loc_set \
0 1 00:01:00 0 days 08:00:00 not considered NaN
1 2 08:01:00 0 days 10:00:00 Food NaN
2 3 10:01:00 0 days 12:00:00 Parks & Outdoors NaN
3 4 12:01:00 0 days 14:00:00 Food NaN
4 5 14:01:00 0 days 18:00:00 Arts & Entertainment NaN
5 6 18:01:00 0 days 20:00:00 Parks & Outdoors NaN
6 7 20:01:00 1 days 00:00:00 Food NaN
end_time_times
0 08:00:00
1 10:00:00
2 12:00:00
3 14:00:00
4 18:00:00
5 20:00:00
6 00:00:00