KDB+ 像 asof 一样在 Pandas 中加入时间序列数据？
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/12322289/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
KDB+ like asof join for timeseries data in pandas?
提问by signalseeker
kdb+ has an ajfunction that is usually used to join tables along time columns.
kdb+ 有一个aj函数，通常用于沿时间列连接表。
Here is an example where I have trade and quote tables and I get the prevailing quote for every trade.
这是一个例子，我有交易和报价表，我得到每笔交易的现行报价。
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
How can I do the same operation using pandas? I am working with trade and quote dataframes where the index is datetime64.
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。
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
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?
我看到Pandas有一个 asof 函数，但它没有在 DataFrame 上定义，只在 Series 对象上定义。我想可以遍历每个系列并将它们一个一个对齐，但我想知道是否有更好的方法？
采纳答案by Chang She
As you mentioned in the question, looping through each column should work for you:
正如您在问题中提到的，遍历每一列应该对您有用：
df1.apply(lambda x: x.asof(df2.index))
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.
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式。
回答by Wes McKinney
I wrote an under-advertised ordered_mergefunction some time ago:
ordered_merge前段时间我写了一个宣传不足的函数：
In [27]: quotes
Out[27]:
time bid ask bsize asize
0 2012-09-06 09:30:00.026000 13.34 13.44 3 16
1 2012-09-06 09:30:00.043000 13.34 13.44 3 17
2 2012-09-06 09:30:00.121000 13.36 13.65 1 10
3 2012-09-06 09:30:00.386000 13.36 13.52 21 1
4 2012-09-06 09:30:00.440000 13.40 13.44 15 17
In [28]: trades
Out[28]:
time price size
0 2012-09-06 09:30:00.439000 13.42 60511
1 2012-09-06 09:30:00.439000 13.42 60511
2 2012-09-06 09:30:02.332000 13.42 100
3 2012-09-06 09:30:02.332000 13.42 100
4 2012-09-06 09:30:02.333000 13.41 100
In [29]: ordered_merge(quotes, trades)
Out[29]:
time bid ask bsize asize price size
0 2012-09-06 09:30:00.026000 13.34 13.44 3 16 NaN NaN
1 2012-09-06 09:30:00.043000 13.34 13.44 3 17 NaN NaN
2 2012-09-06 09:30:00.121000 13.36 13.65 1 10 NaN NaN
3 2012-09-06 09:30:00.386000 13.36 13.52 21 1 NaN NaN
4 2012-09-06 09:30:00.439000 NaN NaN NaN NaN 13.42 60511
5 2012-09-06 09:30:00.439000 NaN NaN NaN NaN 13.42 60511
6 2012-09-06 09:30:00.440000 13.40 13.44 15 17 NaN NaN
7 2012-09-06 09:30:02.332000 NaN NaN NaN NaN 13.42 100
8 2012-09-06 09:30:02.332000 NaN NaN NaN NaN 13.42 100
9 2012-09-06 09:30:02.333000 NaN NaN NaN NaN 13.41 100
In [32]: ordered_merge(quotes, trades, fill_method='ffill')
Out[32]:
time bid ask bsize asize price size
0 2012-09-06 09:30:00.026000 13.34 13.44 3 16 NaN NaN
1 2012-09-06 09:30:00.043000 13.34 13.44 3 17 NaN NaN
2 2012-09-06 09:30:00.121000 13.36 13.65 1 10 NaN NaN
3 2012-09-06 09:30:00.386000 13.36 13.52 21 1 NaN NaN
4 2012-09-06 09:30:00.439000 13.36 13.52 21 1 13.42 60511
5 2012-09-06 09:30:00.439000 13.36 13.52 21 1 13.42 60511
6 2012-09-06 09:30:00.440000 13.40 13.44 15 17 13.42 60511
7 2012-09-06 09:30:02.332000 13.40 13.44 15 17 13.42 100
8 2012-09-06 09:30:02.332000 13.40 13.44 15 17 13.42 100
9 2012-09-06 09:30:02.333000 13.40 13.44 15 17 13.41 100
It could be easily (well, for someone who is familiar with the code) extended to be a "left join" mimicking KDB. I realize in this case that forward-filling the trade data is not appropriate; just illustrating the function.
它可以很容易地（好吧，对于熟悉代码的人）扩展为模仿 KDB 的“左连接”。我意识到在这种情况下向前填充贸易数据是不合适的；只是说明功能。
回答by chrisaycock
pandas 0.19 has introduced an asof join:
pd.merge_asof(trades, quotes, on='time')
The semantics are very similar to the functionality in q/kdb+.
语义与 q/kdb+ 中的功能非常相似。
