KDB+ like asof 加入 pandas 中的时间序列数据?

问题描述

kdb+ 有一个 aj 函数，通常用于沿时间连接表列.

kdb+ has an aj function that is usually used to join tables along time columns.

这是一个示例，其中我有交易和报价表，并且我获得了每笔交易的现行报价.

Here is an example where I have trade and quote tables and I get the prevailing quote for every trade.

q)5# t
time         sym  price size 
-----------------------------
09:30:00.439 NVDA 13.42 60511
09:30:00.439 NVDA 13.42 60511
09:30:02.332 NVDA 13.42 100  
09:30:02.332 NVDA 13.42 100  
09:30:02.333 NVDA 13.41 100  

q)5# q
time         sym  bid   ask   bsize asize
-----------------------------------------
09:30:00.026 NVDA 13.34 13.44 3     16   
09:30:00.043 NVDA 13.34 13.44 3     17   
09:30:00.121 NVDA 13.36 13.65 1     10   
09:30:00.386 NVDA 13.36 13.52 21    1    
09:30:00.440 NVDA 13.4  13.44 15    17

q)5# aj[`time; t; q]
time         sym  price size  bid   ask   bsize asize
-----------------------------------------------------
09:30:00.439 NVDA 13.42 60511 13.36 13.52 21    1    
09:30:00.439 NVDA 13.42 60511 13.36 13.52 21    1    
09:30:02.332 NVDA 13.42 100   13.34 13.61 1     1    
09:30:02.332 NVDA 13.42 100   13.34 13.61 1     1    
09:30:02.333 NVDA 13.41 100   13.34 13.51 1     1  

如何使用 pandas 执行相同的操作?我正在使用索引为 datetime64 的交易和报价数据框.

How can I do the same operation using pandas? I am working with trade and quote dataframes where the index is datetime64.

In [55]: quotes.head()
Out[55]: 
                              bid    ask  bsize  asize
2012-09-06 09:30:00.026000  13.34  13.44      3     16
2012-09-06 09:30:00.043000  13.34  13.44      3     17
2012-09-06 09:30:00.121000  13.36  13.65      1     10
2012-09-06 09:30:00.386000  13.36  13.52     21      1
2012-09-06 09:30:00.440000  13.40  13.44     15     17

In [56]: trades.head()
Out[56]: 
                            price   size
2012-09-06 09:30:00.439000  13.42  60511
2012-09-06 09:30:00.439000  13.42  60511
2012-09-06 09:30:02.332000  13.42    100
2012-09-06 09:30:02.332000  13.42    100
2012-09-06 09:30:02.333000  13.41    100

我看到 pandas 有一个 asof 函数，但它没有在 DataFrame 上定义，只在 Series 对象上.我想可以循环遍历每个系列并将它们一一对齐，但我想知道是否有更好的方法?

I see that pandas has an asof function but that is not defined on the DataFrame, only on the Series object. I guess one could loop through each of the Series and align them one by one, but I am wondering if there is a better way?

推荐答案

正如你在问题中提到的，遍历每一列应该适合你:

As you mentioned in the question, looping through each column should work for you:

df1.apply(lambda x: x.asof(df2.index))

我们可能会创建一个更快的 NaN-naive 版本的 DataFrame.asof 来一次性完成所有列.但就目前而言，我认为这是最直接的方法.

We could potentially create a faster NaN-naive version of DataFrame.asof to do all the columns in one shot. But for now, I think this is the most straightforward way.

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