像asof一样的KDB +可以在 pandas 中获取时间序列数据吗? [英] KDB+ like asof join for timeseries data in pandas?
问题描述
kdb +具有 aj 函数，通常用于沿时间列联接表.
kdb+ has an aj function that is usually used to join tables along time columns.
在此示例中，我有交易表和报价表，并获得每笔交易的通用报价.
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
如何使用熊猫进行相同的操作?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
我看到熊猫具有asof函数，但未在DataFrame上定义，仅在Series对象上定义.我猜一个人可以遍历每个系列并一个一个地对齐它们，但是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
正如您在问题中提到的那样，遍历每一列都应该对您有用:
As you mentioned in the question, looping through each column should work for you:
df1.apply(lambda x: x.asof(df2.index))
我们有可能创建一个更快的NaN天真版本的DataFrame.asof，一次完成所有列.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.
这篇关于像asof一样的KDB +可以在 pandas 中获取时间序列数据吗?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
