我正在尝试在date列上合并两个数据框(都尝试作为类型object或datetime.date,但是无法提供所需的合并输出：
import pandas as pd
df1 = pd.DataFrame({‘amt’: {0: 1549367.9496070854,
1: 2175801.78219801,
2: 1915613.1629125737,
3: 1703063.8323954903,
4: 1770040.7987461537},
‘month’: {0: ‘2015-02-01’,
1: ‘2015-03-01’,
2: ‘2015-04-01’,
3: ‘2015-05-01’,
4: ‘2015-06-01’}})
print(df1)
amt month
0 1.549368e+06 2015-02-01
1 2.175802e+06 2015-03-01
2 1.915613e+06 2015-04-01
3 1.703064e+06 2015-05-01
4 1.770041e+06 2015-06-01
df2 = {‘factor’: {datetime.date(2015, 2, 1): 1.0,
datetime.date(2015, 3, 1): 1.0,
datetime.date(2015, 4, 1): 1.0,
datetime.date(2015, 5, 1): 1.0,
datetime.date(2015, 6, 1): 0.99889679025914435},
‘month’: {datetime.date(2015, 2, 1): datetime.date(2015, 2, 1),
datetime.date(2015, 3, 1): datetime.date(2015, 3, 1),
datetime.date(2015, 4, 1): datetime.date(2015, 4, 1),
datetime.date(2015, 5, 1): datetime.date(2015, 5, 1),
datetime.date(2015, 6, 1): datetime.date(2015, 6, 1)}}
df2 = pd.DataFrame(df2)
print(df2)
factor month
2015-02-01 1.000000 2015-02-01
2015-03-01 1.000000 2015-03-01
2015-04-01 1.000000 2015-04-01
2015-05-01 1.000000 2015-05-01
2015-06-01 0.998897 2015-06-01
pd.merge(df2, df1, how=’outer’, on=’month’)
factor month amt
0 1.000000 2015-02-01 NaN
1 1.000000 2015-03-01 NaN
2 1.000000 2015-04-01 NaN
3 1.000000 2015-05-01 NaN
4 0.998897 2015-06-01 NaN
5 NaN 2015-02-01 1.549368e+06
6 NaN 2015-03-01 2.175802e+06
7 NaN 2015-04-01 1.915613e+06
8 NaN 2015-05-01 1.703064e+06
9 NaN 2015-06-01 1.770041e+06
Original: https://blog.csdn.net/weixin_32504329/article/details/113983349
Author: 捌比特咖啡阚欧礼
Title: python时间数据合并_python-在日期列问题上合并熊猫
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