[英]Pandas dataframe slice indexing by index criterias
I often need to do some searching in the index by some higher/lower than criteria in Pandas DataFrame. 我经常需要以比Pandas DataFrame中的条件高/低的标准在索引中进行一些搜索。 I have found a way to do it, but it feels a bit cumbersome, or somehow unsmart.
我已经找到了一种方法,但是感觉有点麻烦,或者有点不明智。 This is my current method:
这是我目前的方法:
from numpy import linspace
import pandas as pd
df = pd.DataFrame(linspace(1,5,5),index=linspace(0.1,0.5,5))
df
0
0.1 1
0.2 2
0.3 3
0.4 4
0.5 5
df[(df.index>0.3) * (df.index <0.5)]
0
0.3 3
0.4 4
It does give me what I wan't, but please suggest a better way if you have one. 它确实给了我我所不想要的东西,但是如果有的话,请提出一种更好的方法。
I would do it like this. 我会这样做。 Operating with a float-like index is a bit unusual and can give somewhat unexpected results.
使用类似浮点的索引进行操作有点不寻常,并且可能会产生一些意想不到的结果。 In 0.13 (release very shortly), it has much more support, but still operates differently that a 'regular' index.
在0.13(即将发布)中,它具有更多的支持,但运行方式仍与“常规”索引不同。 See here
看这里
In [4]: df = pd.DataFrame({ 'A' : np.linspace(1,5,5), 'B' : np.linspace(0.1,0.5,5) })
In [5]: df
Out[5]:
A B
0 1 0.1
1 2 0.2
2 3 0.3
3 4 0.4
4 5 0.5
In [6]: df.loc[(df.B>0.3)&(df.B<0.5)]
Out[6]:
A B
2 3 0.3
3 4 0.4
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