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
如何获得最接近例如17:00小时（不考虑日期）的每个组元素的索引？理想的结果将是：
>>> df.groupby('type').date. ???
type
A 1
B 3
C 7
Name: date, dtype: int64
另外，如果我想找到最接近但早于给定时间的东西怎么办？再次在17:00时需要返回：
>>> df.groupby('type').date. ???
type
A 1
B 4
C 7
Name: date, dtype: int64
获取默认日期，添加times并获得时间差t：
首先通过将的绝对值的最小索引乘以DataFrameGroupBy.idxmin，对于第二个解决方案，通过将NaNs的正值替换为DataFrameGroupBy.idxmax和来获取每组的最大负值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
详细说明：
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
本文收集自互联网，转载请注明来源。
如有侵权，请联系 [email protected] 删除。
我来说两句