我想合并三列上的两个数据帧：电子邮件、主题和时间戳。 数据帧之间的时间戳不同，因此我需要确定一组电子邮件和主题最接近的匹配时间戳。
下面是一个可重现的示例，使用建议的最接近匹配函数this问题。
import numpy as np
import pandas as pd
from pandas.io.parsers import StringIO
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)
a = """timestamp,email,subject
2016-07-01 10:17:00,[email protected],subject3
2016-07-01 02:01:02,[email protected],welcome
2016-07-01 14:45:04,[email protected],subject3
2016-07-01 08:14:02,[email protected],subject2
2016-07-01 16:26:35,[email protected],subject4
2016-07-01 10:17:00,[email protected],subject3
2016-07-01 02:01:02,[email protected],welcome
2016-07-01 14:45:04,[email protected],subject3
2016-07-01 08:14:02,[email protected],subject2
2016-07-01 16:26:35,[email protected],subject4
"""
b = """timestamp,email,subject,clicks,var1
2016-07-01 02:01:14,[email protected],welcome,1,1
2016-07-01 08:15:48,[email protected],subject2,2,2
2016-07-01 10:17:39,[email protected],subject3,1,7
2016-07-01 14:46:01,[email protected],subject3,1,2
2016-07-01 16:27:28,[email protected],subject4,1,2
2016-07-01 10:17:05,[email protected],subject3,0,0
2016-07-01 02:01:03,[email protected],welcome,0,0
2016-07-01 14:45:05,[email protected],subject3,0,0
2016-07-01 08:16:00,[email protected],subject2,0,0
2016-07-01 17:00:00,[email protected],subject4,0,0
"""
请注意，对于[电子邮件受保护]最接近的匹配时间戳是 10:17:39，而对于[电子邮件受保护]最接近的匹配是 10:17:05。
a = """timestamp,email,subject
2016-07-01 10:17:00,[email protected],subject3
2016-07-01 10:17:00,[email protected],subject3
"""
b = """timestamp,email,subject,clicks,var1
2016-07-01 10:17:39,[email protected],subject3,1,7
2016-07-01 10:17:05,[email protected],subject3,0,0
"""
df1 = pd.read_csv(StringIO(a), parse_dates=['timestamp'])
df2 = pd.read_csv(StringIO(b), parse_dates=['timestamp'])
df1[['closest', 'time_bt_x_and_y']] = df1.timestamp.apply(find_closest_date, args=[df2.timestamp])
df1
df3 = pd.merge(df1, df2, left_on=['email','subject','closest'], right_on=['email','subject','timestamp'],how='left')
df3
timestamp_x email subject closest time_bt_x_and_y timestamp_y clicks var1
2016-07-01 10:17:00 [email protected] subject3 2016-07-01 10:17:05 00:00:05 NaT NaN NaN
2016-07-01 02:01:02 [email protected] welcome 2016-07-01 02:01:03 00:00:01 NaT NaN NaN
2016-07-01 14:45:04 [email protected] subject3 2016-07-01 14:45:05 00:00:01 NaT NaN NaN
2016-07-01 08:14:02 [email protected] subject2 2016-07-01 08:15:48 00:01:46 2016-07-01 08:15:48 2.0 2.0
2016-07-01 16:26:35 [email protected] subject4 2016-07-01 16:27:28 00:00:53 2016-07-01 16:27:28 1.0 2.0
2016-07-01 10:17:00 [email protected] subject3 2016-07-01 10:17:05 00:00:05 2016-07-01 10:17:05 0.0 0.0
2016-07-01 02:01:02 [email protected] welcome 2016-07-01 02:01:03 00:00:01 2016-07-01 02:01:03 0.0 0.0
2016-07-01 14:45:04 [email protected] subject3 2016-07-01 14:45:05 00:00:01 2016-07-01 14:45:05 0.0 0.0
2016-07-01 08:14:02 [email protected] subject2 2016-07-01 08:15:48 00:01:46 NaT NaN NaN
2016-07-01 16:26:35 [email protected] subject4 2016-07-01 16:27:28 00:00:53 NaT NaN NaN
结果是错误的，主要是因为最接近的日期不正确，因为它没有考虑电子邮件和主题。
预期结果是
修改该函数以提供给定电子邮件和主题的最接近的时间戳会很有帮助。
df1.groupby(['email','subject'])['timestamp'].apply(find_closest_date, args=[df1.timestamp])
但这会产生错误，因为该函数没有为组对象定义。 这样做的最佳方法是什么？
请注意，如果合并df1 and df2 on email and subject，那么结果
拥有一切可能relevant时间戳配对：
In [108]: result = pd.merge(df1, df2, how='left', on=['email','subject'], suffixes=['', '_y']); result
Out[108]:
timestamp email subject timestamp_y clicks var1
0 2016-07-01 10:17:00 [email protected] subject3 2016-07-01 10:17:39 1 7
1 2016-07-01 10:17:00 [email protected] subject3 2016-07-01 14:46:01 1 2
2 2016-07-01 02:01:02 [email protected] welcome 2016-07-01 02:01:14 1 1
3 2016-07-01 14:45:04 [email protected] subject3 2016-07-01 10:17:39 1 7
4 2016-07-01 14:45:04 [email protected] subject3 2016-07-01 14:46:01 1 2
5 2016-07-01 08:14:02 [email protected] subject2 2016-07-01 08:15:48 2 2
6 2016-07-01 16:26:35 [email protected] subject4 2016-07-01 16:27:28 1 2
7 2016-07-01 10:17:00 [email protected] subject3 2016-07-01 10:17:05 0 0
8 2016-07-01 10:17:00 [email protected] subject3 2016-07-01 14:45:05 0 0
9 2016-07-01 02:01:02 [email protected] welcome 2016-07-01 02:01:03 0 0
10 2016-07-01 14:45:04 [email protected] subject3 2016-07-01 10:17:05 0 0
11 2016-07-01 14:45:04 [email protected] subject3 2016-07-01 14:45:05 0 0
12 2016-07-01 08:14:02 [email protected] subject2 2016-07-01 08:16:00 0 0
13 2016-07-01 16:26:35 [email protected] subject4 2016-07-01 17:00:00 0 0
您现在可以获取每行时间戳差异的绝对值：
result['diff'] = (result['timestamp_y'] - result['timestamp']).abs()
然后使用
idx = result.groupby(['timestamp','email','subject'])['diff'].idxmin()
result = result.loc[idx]
找到每组差异最小的行['timestamp','email','subject'].
import numpy as np
import pandas as pd
from pandas.io.parsers import StringIO
a = """timestamp,email,subject
2016-07-01 10:17:00,[email protected],subject3
2016-07-01 02:01:02,[email protected],welcome
2016-07-01 14:45:04,[email protected],subject3
2016-07-01 08:14:02,[email protected],subject2
2016-07-01 16:26:35,[email protected],subject4
2016-07-01 10:17:00,[email protected],subject3
2016-07-01 02:01:02,[email protected],welcome
2016-07-01 14:45:04,[email protected],subject3
2016-07-01 08:14:02,[email protected],subject2
2016-07-01 16:26:35,[email protected],subject4
"""
b = """timestamp,email,subject,clicks,var1
2016-07-01 02:01:14,[email protected],welcome,1,1
2016-07-01 08:15:48,[email protected],subject2,2,2
2016-07-01 10:17:39,[email protected],subject3,1,7
2016-07-01 14:46:01,[email protected],subject3,1,2
2016-07-01 16:27:28,[email protected],subject4,1,2
2016-07-01 10:17:05,[email protected],subject3,0,0
2016-07-01 02:01:03,[email protected],welcome,0,0
2016-07-01 14:45:05,[email protected],subject3,0,0
2016-07-01 08:16:00,[email protected],subject2,0,0
2016-07-01 17:00:00,[email protected],subject4,0,0
"""
df1 = pd.read_csv(StringIO(a), parse_dates=['timestamp'])
df2 = pd.read_csv(StringIO(b), parse_dates=['timestamp'])
result = pd.merge(df1, df2, how='left', on=['email','subject'], suffixes=['', '_y'])
result['diff'] = (result['timestamp_y'] - result['timestamp']).abs()
idx = result.groupby(['timestamp','email','subject'])['diff'].idxmin()
result = result.loc[idx].drop(['timestamp_y','diff'], axis=1)
result = result.sort_index()
print(result)
yields
timestamp email subject clicks var1
0 2016-07-01 10:17:00 [email protected] subject3 1 7
2 2016-07-01 02:01:02 [email protected] welcome 1 1
4 2016-07-01 14:45:04 [email protected] subject3 1 2
5 2016-07-01 08:14:02 [email protected] subject2 2 2
6 2016-07-01 16:26:35 [email protected] subject4 1 2
7 2016-07-01 10:17:00 [email protected] subject3 0 0
9 2016-07-01 02:01:02 [email protected] welcome 0 0
11 2016-07-01 14:45:04 [email protected] subject3 0 0
12 2016-07-01 08:14:02 [email protected] subject2 0 0
13 2016-07-01 16:26:35 [email protected] subject4 0 0