您可以找到最接近的日期的一件事是计算第一个DataFrame中每个日期与第二个DataFrame中的日期之间的天数。然后，您可以使用np.argmin来检索具有最小时间增量的日期。
例如：
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from pandas.io.parsers import StringIO
a = """timepoint,measure
2014-01-01 00:00:00,78
2014-01-02 00:00:00,29
2014-01-03 00:00:00,5
2014-01-04 00:00:00,73
2014-01-05 00:00:00,40
2014-01-06 00:00:00,45
2014-01-07 00:00:00,48
2014-01-08 00:00:00,2
2014-01-09 00:00:00,96
2014-01-10 00:00:00,82
2014-01-11 00:00:00,61
2014-01-12 00:00:00,68
2014-01-13 00:00:00,8
2014-01-14 00:00:00,94
2014-01-15 00:00:00,16
2014-01-16 00:00:00,31
2014-01-17 00:00:00,10
2014-01-18 00:00:00,34
2014-01-19 00:00:00,27
2014-01-20 00:00:00,58
2014-01-21 00:00:00,90
2014-01-22 00:00:00,41
2014-01-23 00:00:00,97
2014-01-24 00:00:00,7
2014-01-25 00:00:00,86
2014-01-26 00:00:00,62
2014-01-27 00:00:00,91
2014-01-28 00:00:00,0
2014-01-29 00:00:00,73
2014-01-30 00:00:00,22
2014-01-31 00:00:00,43
2014-02-01 00:00:00,87
2014-02-02 00:00:00,56
2014-02-03 00:00:00,45
2014-02-04 00:00:00,25
2014-02-05 00:00:00,92
2014-02-06 00:00:00,83
2014-02-07 00:00:00,13
2014-02-08 00:00:00,50
2014-02-09 00:00:00,48
2014-02-10 00:00:00,78"""
b = """timepoint,measure
2014-01-01 00:00:00,78
2014-01-08 00:00:00,29
2014-01-15 00:00:00,5
2014-01-22 00:00:00,73
2014-01-29 00:00:00,40
2014-02-05 00:00:00,45
2014-02-12 00:00:00,48
2014-02-19 00:00:00,2
2014-02-26 00:00:00,96
2014-03-05 00:00:00,82
2014-03-12 00:00:00,61
2014-03-19 00:00:00,68
2014-03-26 00:00:00,8
2014-04-02 00:00:00,94
"""
df1 = pd.read_csv(StringIO(a), parse_dates=['timepoint'])
df1.head()
timepoint measure
0 2014-01-01 78
1 2014-01-02 29
2 2014-01-03 5
3 2014-01-04 73
4 2014-01-05 40
df2 = pd.read_csv(StringIO(b), parse_dates=['timepoint'])
df2.head()
timepoint measure
0 2014-01-01 78
1 2014-01-08 29
2 2014-01-15 5
3 2014-01-22 73
4 2014-01-29 40
def find_closest_date(timepoint, time_series, add_time_delta_column=True):
# takes a pd.Timestamp() instance and a pd.Series with dates in it
# calcs the delta between `timepoint` and each date in `time_series`
# returns the closest date and optionally the number of days in its time delta
deltas = np.abs(time_series - timepoint)
idx_closest_date = np.argmin(deltas)
res = {"closest_date": time_series.ix[idx_closest_date]}
idx = ['closest_date']
if add_time_delta_column:
res["closest_delta"] = deltas[idx_closest_date]
idx.append('closest_delta')
return pd.Series(res, index=idx)
df1[['closest', 'days_bt_x_and_y']] = df1.timepoint.apply(
find_closest_date, args=[df2.timepoint])
df1.head(10)
timepoint measure closest days_bt_x_and_y
0 2014-01-01 78 2014-01-01 0 days
1 2014-01-02 29 2014-01-01 1 days
2 2014-01-03 5 2014-01-01 2 days
3 2014-01-04 73 2014-01-01 3 days
4 2014-01-05 40 2014-01-08 3 days
5 2014-01-06 45 2014-01-08 2 days
6 2014-01-07 48 2014-01-08 1 days
7 2014-01-08 2 2014-01-08 0 days
8 2014-01-09 96 2014-01-08 1 days
9 2014-01-10 82 2014-01-08 2 days
df3 = pd.merge(df1, df2, left_on=['closest'], right_on=['timepoint'])
colorder = [
'timepoint_x',
'closest',
'timepoint_y',
'days_bt_x_and_y',
'measure_x',
'measure_y'
]
df3 = df3.ix[:, colorder]
df3
timepoint_x closest timepoint_y days_bt_x_and_y measure_x measure_y
0 2014-01-01 2014-01-01 2014-01-01 0 days 78 78
1 2014-01-02 2014-01-01 2014-01-01 1 days 29 78
2 2014-01-03 2014-01-01 2014-01-01 2 days 5 78
3 2014-01-04 2014-01-01 2014-01-01 3 days 73 78
4 2014-01-05 2014-01-08 2014-01-08 3 days 40 29
5 2014-01-06 2014-01-08 2014-01-08 2 days 45 29
6 2014-01-07 2014-01-08 2014-01-08 1 days 48 29
7 2014-01-08 2014-01-08 2014-01-08 0 days 2 29
8 2014-01-09 2014-01-08 2014-01-08 1 days 96 29
9 2014-01-10 2014-01-08 2014-01-08 2 days 82 29
10 2014-01-11 2014-01-08 2014-01-08 3 days 61 29
11 2014-01-12 2014-01-15 2014-01-15 3 days 68 5
12 2014-01-13 2014-01-15 2014-01-15 2 days 8 5
13 2014-01-14 2014-01-15 2014-01-15 1 days 94 5
14 2014-01-15 2014-01-15 2014-01-15 0 days 16 5
15 2014-01-16 2014-01-15 2014-01-15 1 days 31 5
16 2014-01-17 2014-01-15 2014-01-15 2 days 10 5
17 2014-01-18 2014-01-15 2014-01-15 3 days 34 5
18 2014-01-19 2014-01-22 2014-01-22 3 days 27 73
19 2014-01-20 2014-01-22 2014-01-22 2 days 58 73
20 2014-01-21 2014-01-22 2014-01-22 1 days 90 73
21 2014-01-22 2014-01-22 2014-01-22 0 days 41 73
22 2014-01-23 2014-01-22 2014-01-22 1 days 97 73
23 2014-01-24 2014-01-22 2014-01-22 2 days 7 73
24 2014-01-25 2014-01-22 2014-01-22 3 days 86 73
25 2014-01-26 2014-01-29 2014-01-29 3 days 62 40
26 2014-01-27 2014-01-29 2014-01-29 2 days 91 40
27 2014-01-28 2014-01-29 2014-01-29 1 days 0 40
28 2014-01-29 2014-01-29 2014-01-29 0 days 73 40
29 2014-01-30 2014-01-29 2014-01-29 1 days 22 40
30 2014-01-31 2014-01-29 2014-01-29 2 days 43 40
31 2014-02-01 2014-01-29 2014-01-29 3 days 87 40
32 2014-02-02 2014-02-05 2014-02-05 3 days 56 45
33 2014-02-03 2014-02-05 2014-02-05 2 days 45 45
34 2014-02-04 2014-02-05 2014-02-05 1 days 25 45
35 2014-02-05 2014-02-05 2014-02-05 0 days 92 45
36 2014-02-06 2014-02-05 2014-02-05 1 days 83 45
37 2014-02-07 2014-02-05 2014-02-05 2 days 13 45
38 2014-02-08 2014-02-05 2014-02-05 3 days 50 45
39 2014-02-09 2014-02-12 2014-02-12 3 days 48 48
40 2014-02-10 2014-02-12 2014-02-12 2 days 78 48
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