pandas 熊猫在日期列问题上合并
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/42812216/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
提示：将鼠标放在中文语句上可以显示对应的英文。显示中英文
时间：2020-09-14 03:12:03 来源:igfitidea点击:
pandas merge on date column issue
提问by muon
I am trying to merge two dataframes on date column (tried both as type objector datetime.date, but fails to give desired merge output:
我正在尝试合并日期列上的两个数据框（都尝试将其作为 typeobject或datetime.date，但未能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
回答by jezrael
I think you need first convert both columns to_datetimebecause need same dtypes:
我认为您需要首先转换两列，to_datetime因为需要相同dtypes：
df1.month = pd.to_datetime(df1.month)
df2.month = pd.to_datetime(df2.month)
print (pd.merge(df2, df1, how='outer', on='month'))
factor month amt
0 1.000000 2015-02-01 1.549368e+06
1 1.000000 2015-03-01 2.175802e+06
2 1.000000 2015-04-01 1.915613e+06
3 1.000000 2015-05-01 1.703064e+06
4 0.998897 2015-06-01 1.770041e+06
#convert to str date column
df2.month = df2.month.astype(str)
print (pd.merge(df2, df1, how='outer', on='month'))
factor month amt
0 1.000000 2015-02-01 1.549368e+06
1 1.000000 2015-03-01 2.175802e+06
2 1.000000 2015-04-01 1.915613e+06
3 1.000000 2015-05-01 1.703064e+06
4 0.998897 2015-06-01 1.770041e+06
