在数据子集上方和下方查找多行的有效方法 [英] efficient way to find several rows above and below a subset of data

问题描述

我想知道是否有一种有效的方法可以在行子集的下方和上方获取 X 行.我在下面创建了一个基本实现，但我确信有更好的方法.我关心的子集是buyindex，它是具有买入信号的行的索引.我想在 saleindex 上方和下方获取几行，以验证我的算法是否正常工作.我如何以有效的方式做到这一点?我的方式似乎很迂回.

I'm wondering if there's an efficient way to get X number of rows below and above a subset of rows. I've created a basic implementation below, but I'm sure there's a better way. The subset that I care about is buyindex, which is the indices of rows that have the buy signal. I want to get several rows above and below the sellindex to verify that my algorithm is working correctly. How do I do it in an efficient way? My way seems roundabout.

buyindex = list(data2[data2['buy'] == True].index)

print buyindex [71, 102, 103, 179, 505, 506, 607]

buyindex1 = map(lambda x: x + 1, buyindex)
buyindex2 = map(lambda x: x - 1, buyindex)
buyindex3 = map(lambda x: x - 2, buyindex)
buyindex4 = map(lambda x: x + 2, buyindex)
buyindex.extend(buyindex1)
buyindex.extend(buyindex2)
buyindex.extend(buyindex3)
buyindex.extend(buyindex4)

buyindex.sort()
data2.iloc[buyindex]

UPDATE - 这是数据的结构.我有买入"的指数.但我基本上想获得高于和低于买入的几个指数.

UPDATE - this is the structure of the data. I have the indices of the "buys." but I basically want to get several indices above and below the buys.

VTI upper   lower   sell    buy AboveUpper  BelowLower  date    tokens_left
38   61.25   64.104107   61.341893   False   True    False   True   2007-02-28 00:00:00  5
39   61.08   64.218341   61.109659   False   True    False   True   2007-03-01 00:00:00  5
40   60.21   64.446719   60.640281   False   True    False   True   2007-03-02 00:00:00  5
41   59.51   64.717936   60.050064   False   True    False   True   2007-03-05 00:00:00  5
142  63.27   68.909776   64.310224   False   True    False   True   2007-07-27 00:00:00  5
217  62.98   68.858308   63.587692   False   True    False   True   2007-11-12 00:00:00  5
254  61.90   66.941126   61.944874   False   True    False   True   2008-01-07 00:00:00  5
255  60.79   67.049925   61.312075   False   True    False   True   2008-01-08 00:00:00  5
296  57.02   61.382677   57.371323   False   True    False   True   2008-03-07 00:00:00  5
297  56.15   61.709166   56.788834   False   True    False   True   2008-03-10 00:00:00  5

更新:我根据选择的答案创建了一个通用函数.如果您认为这可以提高效率，请告诉我.

UPDATE: I created a general function based off the chosen answer. Let me know if you think this could be made even more efficient.

def get_test_index(df, column, numbers):  
    """
    builds an test index based on a range of numbers above and below the a specific index you want.
    df = dataframe to build off of 
    column = the column that is important to you. for instance, 'buy', or 'sell' 
    numbers = how many above and below you want of the important index 

    """

    idx_l = list(df[df[column] == True].index)
    for i in range(numbers)[1:]:
        idxpos = data2[column].shift(i).fillna(False)
        idxpos = list(df[idxpos].index)
        idx_l.extend(idxpos)

        idxneg = data2[column].shift(-i).fillna(False)
        idxneg = list(df[idxneg].index)
        idx_l.extend(idxneg)
    #print idx_l
    return sorted(idx_l)

推荐答案

这将是一个非常有效的方法

This will be a very efficient method

In [39]: df = DataFrame(np.random.randn(10,2))

In [41]: start=3

In [42]: stop=4

In [43]: df.iloc[(max(df.index.get_loc(start)-2,0)):min(df.index.get_loc(stop)+2,len(df))]
Out[43]: 
          0         1
1  0.348326  1.413770
2  1.898784  0.053780
3  0.825941 -1.986920
4  0.075956 -0.324657
5 -2.736800 -0.075813

[5 rows x 2 columns]

如果您本质上想要一个任意索引器的功能，只需创建一个列表您想要并传递给 .iloc

If you want essentially a function of arbitrary indexers, just create a list of the ones you want and pass to .iloc

In [18]: index_wanted = [71, 102, 103, 179, 505, 506, 607]

In [19]: from itertools import chain

In [20]: df = DataFrame(np.random.randn(1000,2))

您可能想要独特的

f = lambda i: [ i-2, i-1, i, i+1, i+2 ]

In [21]: indexers = Index(list(chain(*[ f(i) for i in [71, 102, 103, 179, 505, 506, 607] ]))).unique()

In [22]: df.iloc[indexers]
Out[22]: 
            0         1
69   0.792996  0.264597
70   1.084315 -0.620006
71  -0.030432  1.219576
72  -0.767855  0.765041
73  -0.637771 -0.103378
100 -1.087505  1.698133
101  1.007143  2.594046
102 -0.307440  0.308360
103  0.944429 -0.411742
104  1.332445 -0.149350
105  0.165213  1.125668
177  0.409580 -0.375709
178 -1.757021 -0.266762
179  0.736809 -1.286848
180  1.856241  0.176931
181 -0.492590  0.083519
503 -0.651788  0.717922
504 -1.612517 -1.729867
505 -1.786807 -0.066421
506  1.423571  0.768161
507  0.186871  1.162447
508  1.233441 -0.028261
605 -0.060117 -1.459827
606 -0.541765 -0.350981
607 -1.166172 -0.026404
608 -0.045338  1.641864
609 -0.337748  0.955940

[27 rows x 2 columns]

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