在Python中合并具有不同时间频率的系列/数据框 [英] merge series/dataframe with different time frequencies in python

问题描述

我正在尝试合并两个系列，一个小时和另一个分钟(并将索引保​​持在分钟级别):

i am trying to merge two series, one in hour and another in minute (and keep the index at minute level):

Series A:

time
2017-09-01 01:00:00   0.5
2017-09-01 02:00:00   0.4
Freq: H, Name: A, dtype: float64

B系列在分钟内:

Series B

time
2017-09-01 00:00:00         NaN
2017-09-01 00:03:00   -0.000350
2017-09-01 00:06:00    0.000401
Name: B, dtype: float64

我想按小时合并两个系列.所需的合并数据帧将是:

I want to merge the two series by hour. And the desired merged dataframe would be:

time
2017-09-01 00:00:00         NaN       0.5
2017-09-01 00:03:00   -0.000350       0.5
2017-09-01 00:06:00    0.000401       0.5
2017-09-01 01:00:00    0.002301       0.4
2017-09-01 01:03:00    0.005601       0.4

在SO上搜索之后，我发现的一种方法是在系列B中创建一个小时"列，然后使用A的索引和B的小时"列将A联接到B :(参考:

After searching on SO, one way I found is to create an 'hour' column in series B and then join A to B using the index of A and the 'hour' column of B: (reference: Merging/combining two dataframes with different frequency time series indexes in Pandas?)

B = B.to_frame()
B['hour'] = B.index.to_period('H')
merged = B.join(A, on = 'hour', how = 'left')

但是我收到错误消息:

TypeError: Argument 'values' has incorrect type (expected numpy.ndarray, got Index)

有人知道如何解决该问题吗?也许有更好的方法加入两个系列?非常感谢！

Does anyone know how to solve the issue? Or maybe there is a better way of joining the two series? Thanks a lot!

推荐答案

演示:

In [280]: A
Out[280]:
time
2017-09-01 01:00:00    0.5
2017-09-01 02:00:00    0.4
Name: val, dtype: float64

In [281]: B
Out[281]:
time
2017-09-01 00:00:00         NaN
2017-09-01 00:03:00   -0.000350
2017-09-01 00:06:00    0.000401
Name: val, dtype: float64

In [282]: B.to_frame('B').join(A.to_frame('A').set_index(A.index.shift(-1, freq='H')).resample('3T').ffill())
Out[282]:
                            B    A
time
2017-09-01 00:00:00       NaN  0.5
2017-09-01 00:03:00 -0.000350  0.5
2017-09-01 00:06:00  0.000401  0.5

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