Python 将每月和分钟数据帧与 TZ 感知的日期时间索引相结合

Question

我有下面两个时间序列。日期时间索引是 TZ 感知的。

df1:五分钟间隔

    value_1
Timestamp                                   
2009-04-01 10:50:00+09:30   50
2009-04-05 11:55:00+09:30   55
2009-04-23 16:00:00+09:30   0
2009-05-03 10:50:00+09:30   50
2009-05-07 11:55:00+09:30   55
2009-05-11 16:00:00+09:30   0
2009-07-04 02:05:00+09:30   5
2009-07-21 09:10:00+09:30   10
2009-07-30 12:15:00+09:30   15
2010-09-02 11:25:00+09:30   25
2010-09-22 15:30:00+09:30   30
2010-09-30 06:15:00+09:30   15
2010-12-06 11:25:00+09:30   25
2010-12-22 15:30:00+09:30   30
2010-12-28 06:15:00+09:30   15

df2:通过 groupby('Month') 从不同数据集获取的每月间隔。

    value_2
Timestamp               
2009-04-30 00:00:00+09:30   23
2009-07-31 00:00:00+09:30   28
2010-12-31 00:00:00+09:30   23

我想按索引合并两个数据集。 df1 中的任何记录如果与 df​​2 具有相同的月份，则应包含在最终结果中。预期结果如下。

    value_1 value_2
Timestamp                                   
2009-04-01 10:50:00+09:30   50  23
2009-04-05 11:55:00+09:30   55  23
2009-04-23 16:00:00+09:30   0   23
2009-07-04 02:05:00+09:30   5   28
2009-07-21 09:10:00+09:30   10  28
2009-07-30 12:15:00+09:30   15  28
2010-12-06 11:25:00+09:30   25  23
2010-12-22 15:30:00+09:30   30  23
2010-12-28 06:15:00+09:30   15  23

这是我的尝试。

result = pd.concat([df1, df2], axis=1) 
# this combines the datasets, but not like expected, also by including join="outer". With join="inner", no data shown.

result = pd.merge(df1, df2, left_on='value_1', right_index=True)
# this return ValueError: You are trying to merge on Int64 and datetime64[ns, Australia/North] columns. If you wish to proceed you should use pd.concat

# Using @Ben.T
mt_hMF = df1.merge( df2.reset_index().set_index(df2.index.floor('M')),
                         how='left', left_index=True, right_index=True).set_index('Timestamp')
# This gives ValueError: <MonthEnd> is a non-fixed frequency

Answer 1

尝试一下，使用 strftime 为两个数据帧创建临时合并键:

df1.reset_index()\
   .assign(yearmonth=df1.index.strftime('%Y%m'))\
   .merge(df2.assign(yearmonth=df2.index.strftime('%Y%m')))\
   .set_index('Timestamp')\
   .drop('yearmonth', axis=1)

输出:

    value_1  value_2
Timestamp                                  
2009-04-01 10:50:00+09:30       50       23
2009-04-05 11:55:00+09:30       55       23
2009-04-23 16:00:00+09:30        0       23
2009-07-04 02:05:00+09:30        5       28
2009-07-21 09:10:00+09:30       10       28
2009-07-30 12:15:00+09:30       15       28
2010-12-06 11:25:00+09:30       25       23
2010-12-22 15:30:00+09:30       30       23
2010-12-28 06:15:00+09:30       15       23