选择Pandas中感兴趣的行之前和之后的行
[英]Selecting rows before and after rows of interest in Pandas
问题描述
Let's say I have a time series dataframe with a categorical variable and a value: 
假设我有一个带有分类变量和值的时间序列数据帧：
In [4]: df = pd.DataFrame(data={'category': np.random.choice(['A', 'B', 'C', 'D'], 11), 'value': np.random.rand(11)}, index=pd.date_range('2015-04-20','2015-04-30'))
In [5]: df
Out[5]:
category value
2015-04-20 D 0.220804
2015-04-21 A 0.992445
2015-04-22 A 0.743648
2015-04-23 B 0.337535
2015-04-24 B 0.747340
2015-04-25 B 0.839823
2015-04-26 D 0.292628
2015-04-27 D 0.906340
2015-04-28 B 0.244044
2015-04-29 A 0.070764
2015-04-30 D 0.132221
If I'm interested in rows with category A, filtering to isolate them is trivial. 如果我对类别A的行感兴趣，过滤以隔离它们是微不足道的。 But what if I'm interested in the n rows before category A's as well? 但是，如果我对A类之前的n行感兴趣呢？ If n=2, I'd like to see something like: 如果n = 2，我想看到类似的东西：
In [5]: df[some boolean indexing]
Out[5]:
category value
2015-04-20 D 0.220804
2015-04-21 A 0.992445
2015-04-22 A 0.743648
2015-04-27 D 0.906340
2015-04-28 B 0.244044
2015-04-29 A 0.070764
Similarly, what if I'm interested in the n rows around category A's? 同样，如果我感兴趣的是围绕一个类别的第n行呢？ Again if n=2, I'd like to see this: 再次，如果n = 2，我想看到这个：
In [5]: df[some other boolean indexing]
Out[5]:
category value
2015-04-20 D 0.220804
2015-04-21 A 0.992445
2015-04-22 A 0.743648
2015-04-23 B 0.337535
2015-04-24 B 0.747340
2015-04-27 D 0.906340
2015-04-28 B 0.244044
2015-04-29 A 0.070764
2015-04-30 D 0.132221
Thanks! 谢谢！
2 个解决方案
解决方案1
4 2017-02-09 22:01:52
To answer your first question: 回答你的第一个问题：
df[pd.concat([df.category.shift(-i)=='A' for i in range(n)], axis=1).any(axis=1)]
You will hopefully be able to extend the same (perhaps a somewhat clumsy one) approach to cover more cases. 您希望能够扩展相同（可能有点笨拙）的方法来覆盖更多案例。
解决方案2
3 已采纳 2017-02-09 22:14:31
nrows around category A's:n行：
In [223]: idx = df.index.get_indexer_for(df[df.category=='A'].index)
In [224]: n = 1
In [225]: df.iloc[np.unique(np.concatenate([np.arange(max(i-n,0), min(i+n+1, len(df)))
for i in idx]))]
Out[225]:
category value
2015-04-20 D 0.220804
2015-04-21 A 0.992445
2015-04-22 A 0.743648
2015-04-23 B 0.337535
2015-04-28 B 0.244044
2015-04-29 A 0.070764
2015-04-30 D 0.132221
In [226]: n = 2
In [227]: df.iloc[np.unique(np.concatenate([np.arange(max(i-n,0), min(i+n+1, len(df)))
for i in idx]))]
Out[227]:
category value
2015-04-20 D 0.220804
2015-04-21 A 0.992445
2015-04-22 A 0.743648
2015-04-23 B 0.337535
2015-04-24 B 0.747340
2015-04-27 D 0.906340
2015-04-28 B 0.244044
2015-04-29 A 0.070764
2015-04-30 D 0.132221
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