import pandas as pd
df = pd.DataFrame({'date': ['2014-06-22 17:46:00', '2014-06-24 16:52:00', '2014-06-25 20:02:00', '2014-06-25 17:55:00', '2014-07-02 11:36:00', '2014-07-06 12:40:00', '2014-07-05 12:46:00', '2014-07-27 15:12:00'],
'type': ['A', 'A', 'A', 'B', 'B', 'C', 'C', 'C']})
>>> df
date type
0 2014-06-22 17:46:00 A
1 2014-06-24 16:52:00 A
2 2014-06-25 20:02:00 A
3 2014-06-25 17:55:00 B
4 2014-07-02 11:36:00 B
5 2014-07-06 12:40:00 C
6 2014-07-05 12:46:00 C
7 2014-07-27 15:12:00 C
如何获取最接近时间(例如 17:00)(不考虑日期)的每个组元素的索引?期望的结果是:
>>> df.groupby('type').date. ???
type
A 1
B 3
C 7
Name: date, dtype: int64
此外,如果我想找到最接近但早于给定时间的时间怎么办?再次到 17:00 时,需要返回:
>>> df.groupby('type').date. ???
type
A 1
B 4
C 7
Name: date, dtype: int64
最佳答案
获取默认日期,添加时间
s并获取与时间t
的差值:
首先通过 DataFrameGroupBy.idxmin
获取每组绝对值的最小索引,对于第二个解决方案,通过将正值替换为 DataFrameGroupBy.idxmax
的 NaN 来获取每组的最大负值和 mask
:
df = pd.DataFrame({'date': ['2014-06-22 17:46:00', '2014-06-22 16:52:00',
'2014-06-25 20:02:00', '2014-06-25 17:55:00',
'2014-07-02 11:36:00', '2014-07-06 12:40:00',
'2014-07-05 12:46:00', '2014-07-27 15:12:00'],
'type': ['A', 'A', 'A', 'B', 'B', 'C', 'C', 'C']})
#convert column to datetimes
df['date'] = pd.to_datetime(df.date)
t = '17:00:00'
a = pd.to_datetime(df['date'].dt.strftime('%H:%M:%S')) - pd.to_datetime(t)
print (a)
0 00:46:00
1 -1 days +23:52:00
2 03:02:00
3 00:55:00
4 -1 days +18:36:00
5 -1 days +19:40:00
6 -1 days +19:46:00
7 -1 days +22:12:00
Name: date, dtype: timedelta64[ns]
b = a.abs().groupby(df['type']).idxmin()
print (b)
type
A 1
B 3
C 7
Name: date, dtype: int64
c = a.mask(a > pd.Timedelta(0)).groupby(df['type']).idxmax()
print (c)
type
A 1
B 4
C 7
Name: date, dtype: int64
详细信息:
df1 = pd.concat([df, a, a.abs(), a.mask(a > pd.Timedelta(0))], axis=1)
df1.columns = ['date','type','diff','absolute diff','max negative']
print (df1)
date type diff absolute diff max negative
0 2014-06-22 17:46:00 A 00:46:00 00:46:00 NaT
1 2014-06-22 16:52:00 A -1 days +23:52:00 00:08:00 -1 days +23:52:00
2 2014-06-25 20:02:00 A 03:02:00 03:02:00 NaT
3 2014-06-25 17:55:00 B 00:55:00 00:55:00 NaT
4 2014-07-02 11:36:00 B -1 days +18:36:00 05:24:00 -1 days +18:36:00
5 2014-07-06 12:40:00 C -1 days +19:40:00 04:20:00 -1 days +19:40:00
6 2014-07-05 12:46:00 C -1 days +19:46:00 04:14:00 -1 days +19:46:00
7 2014-07-27 15:12:00 C -1 days +22:12:00 01:48:00 -1 days +22:12:00
关于python - 找到每个 pandas 组最接近的时间值,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/48692745/