我正在尝试合并两个包含每周计数的数据框df_1和包含每月平均计数的df_2。
下面显示的每周计数（df_1）。
date Direct_Connection Indirect_Connection
2014-12-07 36 124
2014-12-14 38 111
2014-12-21 28 95
2014-12-28 15 58
2015-01-04 5 9
2015-01-11 22 85
2015-01-18 49 174
2015-01-25 47 171
2015-02-01 54 187
2015-02-08 45 216
2015-02-15 29 125
2015-02-22 22 63
2015-03-01 27 96
每月平均（df_2），如下所示。
date Direct_Connection Indirect_Connection
2014-12-31 29.25 97.0
2015-01-31 30.75 109.75
2015-02-28 37.5 147.75
2015-03-31 31.0 119.0
我想完成的合并将为“直接连接”和“间接连接”都向df_1（每周计数）添加一个“关联的月平均”列
下面是我想要的输出示例。
date Direct_Connection Assoc_dir_avg Indirect_Connection Ass_indir_avg
2014-12-07 36 29.5 124 97
2014-12-14 38 29.5 111 97
2014-12-21 28 29.5 95 97
2014-12-28 15 29.5 58 97
2015-01-04 5 30.5 9 109.75
2015-01-11 22 30.5 85 109.75
2015-01-18 49 30.5 174 109.75
2015-01-25 47 30.5 171 109.75
2015-02-01 54 37.5 187 147.75
2015-02-08 45 37.5 216 147.75
2015-02-15 29 37.5 125 147.75
2015-02-22 22 37.5 63 147.75
2015-03-01 27 31 96 119
以上DataFrames只是整个DF的简短摘录，跨越了很多年。
任何人都能提供的帮助将不胜感激！
答案 0 :(得分：0)
使用-
# Cast to date if not done already
df1['date'] = pd.to_datetime(df1['date'].str.strip(), format='%Y-%m-%d')
df2['date'] = pd.to_datetime(df2['date'].str.strip(), format='%Y-%m-%d')
from pandas.tseries.offsets import MonthEnd
df=df1.merge(df2, left_on=(df1['date'] + MonthEnd(1)), right_on='date').rename(columns={'Direct_Connection_x':'Direct_Connection',
'Indirect_Connection_x':'Indirect_Connection',
'Direct_Connection_y':'Assoc_dir_avg',
'Indirect_Connection_y':'Ass_indir_avg',
'date_x':'date'}).drop('date_y', axis=1)
输出
date Direct_Connection Indirect_Connection \
0 2014-12-07 36 124.0
1 2014-12-14 38 111.0
2 2014-12-21 28 95.0
3 2014-12-28 15 58.0
4 2015-01-04 5 9.0
5 2015-01-11 22 85.0
6 2015-01-18 49 174.0
7 2015-01-25 47 171.0
8 2015-02-01 54 187.0
9 2015-02-08 45 216 NaN
10 2015-02-15 29 125.0
11 2015-02-22 22 63.0
12 2015-03-01 27 96.0
Assoc_dir_avg Ass_indir_avg
0 29.25 97.00
1 29.25 97.00
2 29.25 97.00
3 29.25 97.00
4 30.75 109.75
5 30.75 109.75
6 30.75 109.75
7 30.75 109.75
8 37.50 147.75
9 37.50 147.75
10 37.50 147.75
11 37.50 147.75
12 31.00 119.00